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Verband der Automobilindustrie

Quality Management in the Automotive Industry

5

Capability of Measurement Processes Capability of Measuring Systems Capability of Measurement Processes Expanded Measurement Uncertainty Conformity Assessment

nd

2

completely revised edition 2010, updated July 2011

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Capability of Measurement Processes

Capability of Measuring Systems Capability of Measurement Processes Expanded Measurement Uncertainty Conformity Assessment

Second completely revised edition 2010, up-dated July 2011 Verband der Automobilindustrie e.V. (VDA)

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ISSN 0943-9412 Printed: 07/2011 This version corresponds to the modified German version of July 2011 A change data sheet VDA 5 / 2011 versus VDA 5 / 2010, can be downloaded, access see page 166

Copyright 2011 by Verband der Automobilindustrie e.V. (VDA) Qualitäts Management Center (QMC) 10117 Berlin, Behrenstraße 35 Germany Overall production: Henrich Druck und Medien GmbH 60528 Frankfurt am Main, Schwanheimer Straße 110 Germany Printed on chlorine-free bleached paper Dokument wurde bereitgestellt vom VDA-QMC Internetportal am 18.08.2011 um 01:03

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Noncommittal VDA recommendation regarding standards The German Association of the Automotive Industry (Verband der Automobilindustrie e.V. - VDA) advises its members to apply the following recommendation regarding standards in implementing and maintaining QM systems. Exclusion of liability VDA Volume 5 is a recommendation that is free for anyone to use. Anyone using it has to ensure that it is applied correctly in each individual case. VDA Volume 5 considers the latest state of the art at the date of publication. The application of the VDA recommendation does not absolve users from their personal responsibility for their own actions. Users are acting at their own risk. The VDA and anyone involved in providing this VDA recommendation exclude liability for any damage. Anyone using this VDA recommendation is asked to inform the VDA in case of detecting any incorrect or ambiguous information in order that the VDA can fix possible errors. References to standards The individual standards referred to by their DIN standard designation and their date of issue are quoted with the permission of the DIN (German Institute for Standardization). It is essential to use the latest issue of the standards, which are available at Beuth Verlag GmbH, 10772 Berlin, Germany. Copyright This document is protected by copyright. Any use outside the strict limits stipulated by copyright law is prohibited without the consent of the VDA and is punishable by law. This applies particularly with regard to copying, translating, microfilming, storing and processing the document in electronic systems. Translations This document will also be translated into other languages. Please contact the VDA-QMC for information about the latest translations.

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Preface Different standards and guidelines contain requirements for estimating and considering the measurement uncertainty. In this regard, companies have to face various questions in implementing and certifying their quality management system. This document explains how to meet these various requirements. A work group of the automotive and supplier industry created VDA Volume 5. It applies to all parts of this branch of industry. The procedures described in this document are based on the ISO/IEC Guide 98-3 (Guide to the expression of uncertainty in measurement) (GUM) [22] and on ISO/TS 14253 (Inspection by measurement of work pieces and measuring equipment, Part 1: Decision rules for proving conformance or non-conformance with specifications) [13]. VDA Volume 5 also contains the well-established and widely used procedures of the MSA manual [1] that are used in order to evaluate and accept measuring equipment. It provides some information about the validation of measurement software as well. In order to ensure the functionality of technical systems, single parts and assemblies have to keep specified tolerances. The following aspects must be considered when determining the necessary tolerances in the construction process: • The functionality of the product must be ensured. • Single parts and assemblies must be produced in a way that they can be assembled easily. • For economic reasons, the tolerances should be as wide as possible, but for functionality reasons, it should be as narrow as necessary. • The expanded measurement uncertainty must be considered in statistical tolerancing. Due to the measurement uncertainty, the range around the specification limits does not allow for a reliable statement about conformance or nonconformance with specified tolerances. This might lead to an incorrect evaluation of measurement results. For this reason, it is important to consider the uncertainty of the measuring system and the measurement process as early as in the planning phase. VDA Volume 5 primarily refers to the inspection of geometrical quantities. Whether or not the approach explained in this document is suitable for measuring physical quantities must be checked in each individual case.

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Our thanks go to the following companies and, in particular, to the people involved for their commitment in creating this document: BMW Group, Munich Daimler AG, Untertürkheim Daimler AG, Sindelfingen GKN Driveline Offenbach KFMtec Methodenentwicklung, Stuttgart MAN Nutzfahrzeuge Aktiengesellschaft, Munich MQS Consulting, Oberhaid Q-DAS GmbH & Co. KG, Weinheim Robert Bosch GmbH, Stuttgart Volkswagen AG, Wolfsburg Volkswagen AG, Kassel VW Nutzfahrzeuge, Hanover

We also would like to thank everyone who provided their suggestions and helped us to improve this document.

Berlin, September 2010 Berlin, July 2011

Verband der Automobilindustrie e. V. (VDA)

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Table of Contents

Page

Preface 1

Standards and Guidelines

5 10

2

Benefits and Field of Application

11

3 3.1

Terms and Definitions General Terms and Definitions

13 13

3.2

Proof of Conformance or Non-conformance with Tolerances according to ISO/TS 14253 [13] 22

4 4.1

General Procedure for Establishing the Capability of Measurement Processes 26 Influences Causing the Uncertainty of Measurement Results 26

4.2

General Information

29

4.3

Specific Approaches

30

4.3.1

Measurement Errors

30

4.3.2

Long-term Analysis of Measurement Process Capability

32

4.3.3

Reproducibility of Identical Measuring Systems

32

4.4

Standard Uncertainties

33

4.4.1

Type A Evaluation (Standard Deviation)

34

4.4.2

Type A Evaluation (ANOVA)

34

4.4.3

Type B Evaluation

36

4.4.3.1

Type B Evaluation: Expanded Measurement Uncertainty UMP Known 37

4.4.3.2

Type B Evaluation: Expanded Measurement Uncertainty UMP Unknown

37

4.5

Combined Standard Uncertainty

38

4.6

Expanded Measurement Uncertainty

38

4.7

Calculation of Capability Ratios

40

4.8

Minimum Possible Tolerance for Measuring Systems / Measurement Processes

44

4.9

Uncertainty Budget

45

4.10

Capability of the Measurement and Production Processes

46

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Dealing with Not Capable Measuring Systems / Measurement Processes

47

5 5.1

Measurement Process Capability Analysis Basic Principles

49 49

5.2

Capability Analysis of a Measuring System

50

5.2.1

Resolution of the Measuring System

53

5.2.2

Repeatability, Systematic Measurement Error, Linearity

53

5.2.2.1

Estimating the Systematic Measurement Error and Repeatability according to the “Type 1 Study”

54

5.2.2.2

Linearity Analysis with Correction on the Measuring Instrument

60

5.3

Measurement Process Capability Analysis

64

5.3.1

Example for Determining the Uncertainty Components of the Measurement Process 69

6 6.1

Ongoing Review of the Measurement Process Capability 71 General Review of the Measurement Stability 71

6.2

Correcting the Regression Function

72

7 7.1

Practical Guidance to Determining Typical Standard Uncertainties Overview of Typical Measurement Process Models

74 79

8 8.1

Special Measurement Processes Measurement Process with Small Tolerances

81 81

8.2

Classification

81

8.3

Validation of Measurement Software

82

9 9.1

Capability Analysis of Attribute Measurement Processes 85 Introduction 85

9.2

Capability Calculations without Using Reference Values

9.3

Capability Calculations Using Reference Values

88

9.3.1

Calculation of the Uncertainty Range

88

9.3.2

Ongoing Review

92

10

Appendix

93

4.11

8

86

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Annex A

Statistical Background of the Measurement Process Capability Analysis

93

Annex B

Estimation of Standard Uncertainties from Temperature 98

Annex C

Reducing the Measurement Uncertainty by Repeating and Averaging Measurements 105

Annex D

k Factors

107

Annex E

Setting Working Point(s)

108

Annex F

Calculation Examples

110

11

Index of Formula Symbols

154

12

Bibliography

158

13

Index

163

14

Downloads

166

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1

Standards and Guidelines

Relevant quality management standards and guidelines require knowledge of the measurement uncertainty or a capability analysis of the measuring and test equipment (qualification of the measuring and test equipment for the respective measurement process). The documents listed in Table 1 contain requirements for measurement processes.

Aim Implementation of QM systems

International/national standards and documents • DIN EN ISO 9000ff [10][11];

Industry standards • VDA Volume 6, Part 1[26]

• ISO 10012 [12]; • EN ISO/IEC 17025 [19]; • ISO/TS 16949 [23]

Estimation of the measurement uncertainty

Metrology, general: • DIN 1319 [5][6][7]; • ISO/IEC Guide 98-3 (GUM) [22]

• standards of technical associations • DKD-3 [2]

Dimension measurement: • attachment 1 to ISO 14253-1 [14] Calculation of the capability of measuring instruments and measuring equipment Consideration of the measurement uncertainty

Table 1:

• QS-9000/ MSA [1] • DIN 55319-3 [8]

• corporate standard

• ISO/WD 22514-7 [24] • ISO/TS 14253-1 [13]

• QS-9000/ MSA [1] • corporate standards

Aims specified in certain standards, recommendations and guidelines to the evaluation of measuring equipment

The aim of VDA Volume 5 is to summarize the requirements and procedures of the existing standards and guidelines in order to gain a standardized and practice-oriented model for the estimation and consideration of the expanded measurement uncertainty. The methods and capability analysis (see MSA [1]) established in practice may be integrated where applicable. Table 14 provides answers to typical questions regarding the estimation of standard measurement uncertainties and the expanded measurement uncertainty.

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2

Benefits and Field of Application

Measuring systems and measurement processes require an adequate and comprehensive evaluation. This evaluation has to include the consideration of influencing quantities such as the calibration uncertainty on the reference standards and its traceability to a national or an international measurement standard, the influence of the test part or the long-term stability of a measuring instrument in the measurement process. If the capability of a measurement process is not established, measurement processes that are “not capable“ might be released. This could cause high consequential costs for corrective action and for the on-going review of a production process using SPC. Moreover, an inspection of the measuring systems could lead to discussions and additional, more complex inspections. The benefits from a qualified measurement process are great, because reliable and correct measurement results form the basis of important decisions, such as whether • to release or not to release a manufacturing device or measuring equipment. • to take or not to take corrective action in a running production process. • to accept or to reject a product. • to deliver, to rework or to scrap a product. Furthermore, in the case of product liability, it is required to give proof of the capability of the measurement processes used in order to manufacture and release the product. If this proof cannot be provided, the measurement results, that the evaluation of the products is based on, will always be contested. In the end, it is important to know that the expression of the measurement uncertainty is not a negative criterion or a deficit. It describes the actual quality or safety of a measurement result. For this reason, the measurement uncertainty is not referred to as “measuring error” in this document, as is often the case in literature. The measurement uncertainty is a piece of additional information in order to complete the measurement result. It must not be mistaken for an incorrect measurement result. VDA Volume 5 refers to repeatable processes measuring geometrical characteristics, such as the measurement of lengths and angles.

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Its applicability to destructive tests, rapidly changing measured quantity values or other physical quantities has not been validated and must be verified in each individual case. In addition, this document describes practical procedures in order to make a measurement systems analysis and to calculate the measurement uncertainty of measurement processes. It deals with the following issues: •

capability of measuring systems

•

short-term evaluation of the capability of entire measurement processes (with and without the influence of the test parts‘ form deviation, acceptance of measuring systems (measuring instruments), comparison of several places of measurement, measuring systems for the same measurement tasks)

•

long-term analysis of the capability of entire measurement processes over a significant period (e.g. for several days)

•

determination of the expanded measurement uncertainty in order to consider information about conformity according to ISO/TS 14253 Part 1 [13]

•

ongoing evaluation of the capability of a measurement process (stability of a measuring instrument)

It is also about specific features, such as •

test characteristics with narrow tolerances

•

classifications.

Within the quality management system, it is important to determine the field of application of this document, i.e. the processes or characteristics it applies to. A schematic approach helps to reach the reproducibility of the test results and facilitates its application in practice for users. This document is an enhanced version of the VDA Volume 5 “Capability of Measurement Processes“, 2003 edition. Its basic approach is to compare the measurement uncertainty or components of it, to the tolerance to be tested and to use this ratio as evaluation criterion. The procedures of the MSA manual (Measurement Systems Analysis) [1] established in practice can be included.

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3

Terms and Definitions

3.1

General Terms and Definitions

The following sections define the most important terms used in this document. Moreover, the terms and definitions according to ISO 3534-1 [9], ISO 10012 [12], VIM (International vocabulary of metrology) [21], ISO/IEC Guide 98-3 (GUM) [22], ISO/TS 14253 [13] and DIN 1319 [5] [6] [7] are applied. The definitions of most of the following terms are taken from standards (see reference). Colloquially, some other expressions are often used for some of the terms defined in this chapter. These expressions are added in parentheses. They are also used in the text. Measurement uncertainty [22] Parameter, associated with the result of a measurement that characterizes the dispersion of the values that could reasonably be attributed to the measurand. Note 1:

The parameter may be, for example, a standard deviation (or a given multiple of it), or the half-width of an interval having a stated level of confidence.

Note 2:

Uncertainty of measurement comprises, in general, many components. Some of these components may be evaluated from the statistical distribution of the results of series of measurements and can be characterized by experimental standard deviations. The other components, which can also be characterized by standard deviations, are evaluated from assumed probability distributions based on experience or other information.

Note 3:

It is understood that the result of the measurement is the best estimate of the value of the measurand and that all components of uncertainty, including those arising from systematic effects, such as components associated with corrections and reference standards, contribute to the dispersion.

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Standard uncertainty u(xi) [22] (standard measurement uncertainty or uncertainty component) Uncertainty of the result of a measurement expressed as a standard deviation. Uncertainty budget (for a measurement or calibration) Table summarizing the results of the estimations or statistical evaluations regarding the uncertainty components contributing to the uncertainty of a measurement result (see Table 5). Note 1:

The uncertainty of a measurement result is only clear if the measurement procedure (including the test part, measurand, measurement method and conditions of measurement) is defined.

Note 2:

The designation “budget” is associated with numerical values attributed to the uncertainty components, their combinations and extension based on the measurement procedure, the conditions of measurement and assumptions.

Combined standard uncertainty u(y) [22] (combined standard measurement uncertainty) Standard uncertainty of the result of a measurement when that result is obtained from the values of a number of other quantities, equal to the positive square root of a sum of terms, the terms being the variances or covariances of these other quantities weighted according to how the measurement result varies with changes in these quantities.

Coverage factor k [22] Numerical factor used as a multiplier of the combined standard uncertainty in order to obtain an expanded uncertainty (see Table 4 and Annex D). UMS or UMP = k · u(y)

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Expanded measurement uncertainty (expanded uncertainty) [22] Quantity defining an interval about the result of a measurement that may be expected to encompass a large fraction of the distribution of values that could reasonably be attributed to the measurand. Note 1:

The fraction may be viewed as the coverage probability or level of confidence of the interval.

Note 2:

To associate a specific level of confidence with the interval defined by the expanded uncertainty requires explicit or implicit assumptions regarding the probability distribution characterized by the measurement result and its combined standard uncertainty. The level of confidence that may be attributed to this interval can be known only to the extent to which such assumptions may be justified.

Remark:

The GUM [22] and ISO/TS 14253 [13] use the formula symbol U for the expanded measurement uncertainty. The latest standards, such as 3534-2 [9], refer to the upper tolerance limit as U. In order to avoid confusions, this document uses the symbol UMS for the expanded measurement uncertainty where the text refers to a measuring system and UMP where the text refers to a measurement process.

Testing (conformity assessment) [17] Determining one or more characteristics on an object included in the conformity assessment, according to a certain procedure. Conformity [10] Fulfilment of a requirement. Operator [18] Person possessing the relevant professional and personal qualifications in order to conduct an inspection and evaluate the results. Test characteristic [20] Characteristic the inspection is based on.

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Characteristic [21] Distinguishing feature. Value of the characteristic (measured quantity value) yi [20] Form of the value attributed to the characteristic. Measurement result (result of measurement) Y [21] Set of quantity values being attributed to a measurand together with any other available relevant information. Note:

A measurement result is generally expressed as a single measured quantity and a measurement uncertainty Y = y i ± U MP . If the measurement uncertainty is considered negligible for some purpose, the measurement result may be expressed as a single measured quantity value. In many fields, this is the common way of expressing a measurement value.

Bias / Bi [21] Estimate of a systematic measurement error. MSA [1] MSA refers to Measurement Systems Analysis. The MSA manual presents guidelines of the QS-9000 for the assessment and acceptance of measuring equipment. ANOVA ANOVA (Analysis of Variance) represents a mathematical approach in order to determine variances. Based on these variances, standard uncertainties can be estimated. Measurement repeatability (repeatability) [21] Measurement precision under a set of repeatability conditions of measurement. Intermediate measurement precision (intermediate precision) [21] Measurement precision under a set of intermediate precision conditions of measurement.

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Inspection by variables (measuring) Determination of a specific value of a measurand as a multiple or a component of an item or of a specified reference system. Measuring means to draw a quantitative comparison between the measurand and the reference value by using a measuring instrument or measuring equipment. Inspection by attributes (gauging) Comparison of a test part to a gauge in order to find out whether a specified limit is exceeded. The actual deviation of the tested quantity from the nominal quantity value is not determined. True quantity value (true value) [21] Value consistent with the definition of an observed, specific quantity. Note 1:

This value would be obtained by a perfect measurement.

Note 2:

True values are by nature indeterminate.

Conventional true value (of a quantity) [22] Value attributed to a particular quantity and accepted, sometimes by convention, as having an uncertainty appropriate for a given purpose. Note 1:

Conventional true value is sometimes called assigned value, best estimate of the value, conventional value or reference value.

Note 2:

Frequently, a number of results of measurements of a quantity are used to establish a conventional true value.

Measurement standard [21] Realization of the definition of a given quantity, with stated quantity value and associated measurement uncertainty used as a reference. Working measurement standard (working standard) [21] Measurement standard that is used routinely to calibrate or verify measuring instruments and measuring systems.

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Calibration [21] Operation that, under specified conditions, in a first step, establishes a relation between the quantity values with measurement uncertainties provided by measurement standards and corresponding indications with associated measurement uncertainties and, in a second step, uses this information to establish a relation for obtaining a measurement result from an indication. Note:

Calibration should not be confused with adjustment of a measuring system, often mistakenly called “self-calibration“.

Remark:

Comparison measurement taken under specified conditions between a more precise calibration device and the object to be calibrated in order to estimate the systematic measurement error.

Adjustment [21] Set of operations carried out on a measuring system so that it provides prescribed indications corresponding to given values of a quantity to be measured. Note 1:

Adjustment of a measuring system should not be confused with calibration, which is a prerequisite for adjustment..

Note 2:

After an adjustment of a measuring system, the measuring system must usually be recalibrated.

Remark:

Elimination of the systematic measurement error of the object to be calibrated are estimated in the calibration. Adjustment includes all actions required in order to minimize the deviation of the final indication.

Metrological traceability [21] and [3] Property of a measurement result whereby the result can be related to a reference through a documented unbroken chain of calibrations, each contributing to the measurement uncertainty.

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Setting Setting means to set measuring systems to a measure referring to material measures. If the aim of this operation is a zero indication, it is referred to as zero setting. Remark:

Setting means to transfer the calibrated actual value of the working measurement standard (material measure) to the measuring instrument under real operating conditions. Users make their measuring instruments ready for operation on site. Adjustment minimizes systematic measurement errors.

Measuring instrument [21] Device used for making measurements, alone or in conjunction with one or more supplementary devices. Note 1:

A measuring instrument that can be used alone is a measuring system.

Note 2:

A measuring instrument may be an indicating measuring instrument or a material measure.

Measuring equipment [10] Measurement instrument, software, measurement standard, reference material or auxiliary apparatus or combination thereof necessary to realize a measurement process. Resolution [21] The smallest change in a quantity being measured that causes a perceptible change in the corresponding indication. Measuring system [21] Set of one or more measuring instruments and often other devices, including any reagent and supply, assembled and adapted to give information used to generate measured quantity values within specified intervals for quantities of specified kinds.

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Capability of the measuring system Qualification of the measuring system for a specific measurement task exclusively taking into account the required accuracy of measurement (measurement uncertainty UMS) (see Chapter 4.7). Maximum permissible measurement error (error limit) MPE [21] Extreme value of measurement error, with respect to a known reference quantity value, permitted by specifications or regulations for a given measurement, measuring instrument, or measuring system. Note:

Usually, the term “maximum permissible errors“ or “limits of error” is used where there are two extreme values.

Measurement process [21] Interaction of interrelated operating resources, actions and influences creating a measurement. Note:

Operating resources can be both, human and materials.

Measurement process capability Qualification of the measurement process for a specific measurement task exclusively taking into account the required accuracy of measurement (expanded measurement uncertainty UMP) (see Chapter 4.7). Remark:

In general, the measuring system or measurement process capability analysis is a short-term evaluation. Especially in case of new measuring systems or measurement processes, the stability of a measuring instrument should be determined over a significant period and considered in order to prove capability.

Stability of a measuring instrument (stability) [21] Property of a measuring instrument, whereby its metrological properties remain constant in time. Note:

20

Stability may be quantified in several ways:

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Example 1:

In terms of the duration of a time interval over which a metrological property changes by a stated amount.

Example 2:

In terms of the change of a property over a stated time interval.

Remark:

Inspection of the stability must be demonstrated by means of an ongoing review of the capability of the measurement process (see Chapter 6).

Specified Tolerance [9] Difference between the upper specification limit U and lower specification limit L. Verification [21] Provision of objective evidence that a given item fulfils specified requirements. Example 1:

Confirmation that a given reference material as claimed is homogeneous for the quantity value and measurement procedure concerned, down to a measurement portion having a mass of 10 mg.

Example 2:

Confirmation that a target measurement uncertainty can be met.

Validation [21] Verification, where the specified requirements are adequate for an intended use. Example 1:

A measurement process must be determined with sufficient accuracy due to its interpretation of the “diameter” level. Validation ensures the capability of the measurement process needed for the specified size of the diameter (e.g. nominal value) and the demanded tolerance.

Example 2:

see Chapter 8.3

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Control chart Control chart, also referred to as quality control chart or QCC, is applied to statistical process control. A QCC generally consists of a “level” path and a “variation” path together with specified action limits. Statistical values such as sample means and sample standard deviations are plotted on the respective path of the QCC.

3.2

Proof of Conformance or Non-conformance with Tolerances according to ISO/TS 14253 [13]

Part 1 of ISO/TS 14253 establishes the rules for determining when the characteristics of a specific work piece or measuring equipment are in conformance or non-conformance with a given tolerance (for a work piece) or limits of maximum permissible errors (for measuring equipment), taking into account the uncertainty of measurement.

increasing measurement uncertainty UMP

It also gives rules on how to deal with cases where a clear decision (conformance or non-conformance with specification) cannot be taken, i.e. when the measurement result falls within the uncertainty range (see Figure 1) that exists around the tolerance limits. phase U specification (construction)

L

conformance zone

non-conformance zone

non-conformance zone

verification uncertainty range

uncertainty range

phase (production)

work piece tolerance outside the tolerance

Figure 1:

22

within the tolerance

outside the tolerance

Uncertainty ranges and conformance or non-conformance zones

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Conformance Fulfilment of specified requirements. Conformance zone Specification zone reduced by the expanded uncertainty of measurement UMP (Figure 2). Note:

The specification is reduced by the expanded uncertainty of measurement UMP at the upper and lower specification limits. In case of characteristics with a one-sided specification, this reduction does not apply to the natural boundary side.

Proof of conformance If the measurement result Y (measured quantity value yi associated with the expanded measurement uncertainty UMP) is lying within the specification zone, the conformance with the tolerance is proved and the product can be accepted. measurement result Y

UM P

U MP

measurement value yi

tolerance

L lower tolerance limit

Figure 2:

U upper tolerance limit

Proof of conformance with the tolerance

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Non-conformance Non-fulfilment of a specified requirement. Non-conformance zone Zone(s) outside the specification zone extended by the expanded uncertainty of measurement UMP (Figure 1). Note:

The specification is extended by the expanded uncertainty of measurement UMP at the upper and lower specification limit. In case of characteristics with a one-sided specification, this reduction does not apply to the natural boundary side.

Proof of non-conformance Non-conformance with the tolerance is proved when the measurement result Y (measured quantity value yi associated with the expanded measurement uncertainty UMP) is lying beyond the specification zone (Figure 3). In this case, the work piece must be rejected. measurement result Y

UM P

tolerance

L lower tolerance limit

Figure 3:

24

U MP

measurement value yi

U upper tolerance limit

Proof of non-conformance with the tolerance

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Uncertainty ranges Areas near the specification limits where conformance or non-conformance cannot clearly be determined because of the measurement uncertainty (Figure 1). When the measurement result Y (measured quantity value yi associated with the expanded measurement uncertainty UMP) includes one of the specification limits, neither conformance or non-conformance can be proved (Figure 4). Note 1:

Uncertainty ranges are symmetrical to the specification limits.

Note 2:

As a result, work pieces can neither be automatically accepted nor rejected. For such “dead end cases”, it is advisable to follow the rule below:

Reduce the uncertainty of measurement and thereby reduce the uncertainty range in order that conformance or non-conformance can clearly be demonstrated. Mutual agreement between customers and manufacturers: measurement result Y

UM P

tolerance

L lower tolerance limit

Figure 4:

U MP

measurement value yi

U upper tolerance limit

Conformance or non-conformance with the tolerance can be proved

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4

General Procedure for Establishing the Capability of Measurement Processes

Inspections for series production control and conformity assessments require characteristics that are identified correctly as characteristics in conformance, i.e. “o.k.” (within the specification limits), or in non-conformance, i.e. “n.o.k.” (beyond the specification limits), with the tolerance. It is important to consider the measurement error caused by the variation of the production process as well as errors caused by the measurement process. Measurement errors caused by the measurement process lead to an uncertain measurement result and thus to dubious decisions. Errors must be known and can only be accepted to a certain degree relating to the specified tolerance of the inspection. 4.1

Influences Causing the Uncertainty of Measurement Results

Influences caused by measuring systems, operators, test parts, environment, etc. usually affect the measurement result (see Figure 5) as random errors. Evaluation Method

Object

Man

Mathemat. models

Material Motivation

Qualification

Care

Calibration/ justification

Measuring range tactile touch

Time/cost Stability

Measuring points layout

not recorded bias

Measurement Procedure

Figure 5:

26

Setting uncertainty

Resolution

Gage

Mounting Fixture

Surface texture

Stability

random measurement deviations

Capacity Measuring points total

Vibrations Soiling

Measurement Result

Sensibility

contact-free

Illumination Humidity

Statistical method

Accessibility

Voltage Electricity

Temperature

Computer application

Surface

Discipline

Psychical constitution

Pressure

Measurement value composition

Shape Physical constitution

Environment

Shape Location

Position

Type of master Shape/Position

Measurement stability

Master

Important influences on the uncertainty of measurement results

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The following sections provide some examples of frequently recurring and important influence components that are described in detail in Chapter 5 and Table 14. Measurement standard / reference standard Depending on the quality of the measurement standard, it could lead to a considerable proportion of the uncertainty of the measurement result. The calibration certificate normally contains the respective uncertainty. The traceability of the standard used must be demonstrated. Measuring equipment / measuring system Important influence components associated with the measuring system are •

resolution

•

reference standard

•

setting to one or several test parts

•

linearity deviation / systematic measurement error

•

measurement repeatability

Environment Important influence components of the environment affecting the measurement process are •

temperature

•

lighting

•

vibrations

•

contamination

•

humidity

The influence of temperature variations on a test part, measuring system and clamping device are particularly significant in terms of environmental conditions. In case of measurements of lengths, this fact leads to different measurement results when the temperature changes. Table 11 and Annex B provide recommendations for the determination of the standard measurement uncertainty from temperature.

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Human / operator Influences of operators leading to the uncertainty of measurement results are caused by the different qualifications and skills of operators in taking measurements. • different measuring forces • reading errors because of parallaxes • physical and psychological constitution of the operator • qualification, motivation and care Test part Influences from test parts can be detected when, for example, the same characteristic is measured at different points on the test part. It results from, for example: • geometrical deviations (form deviations and changes in the surface texture) • material properties (e.g. elasticity) • lack of inherent stability Measurement method / measurement procedure The way a measurement is taken or the selected sampling strategy has an impact on the measurement result. Even the applied mathematical procedures for determining a measured quantity value are influencing the result. Mounting device If measuring instruments are built into installations, they will also affect the measurement result. Evaluation method The mathematical and statistical procedures used for evaluation (e.g. elimination of detected outliers or filtering) can have an effect on the result.

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4.2

General Information

The evaluation of measurement processes and the consideration of the measurement uncertainty are based on the following table (Table 2). Input information

Description

Information about the measuring system, the test characteristic and about the measurement standards (references) Information about the measurement process and the test characteristic including all uncertainty components to be considered

Measuring system capability analysis

Measurement process capability analysis

Result Expanded measurement uncertainty UMS capability ratio QMS (see Chapter 5.2) Expanded measurement uncertainty UMP capability ratio QMP (see Chapter 5.3)

Information about the test characteristic and the corresponding expanded measurement uncertainty UMP

Conformity assessment including the expanded measurement uncertainty

Conformance or nonconformance zone (see ISO/TS 14253 [13])

Information from measuring system, measurement process and about the test characteristic

Ongoing review of the capability of the measurement process

Control chart including the calculated action limits (see Chapter 6)

Table 2:

General procedures for establishing the capability of measurement processes

In order to prove the capability of a measurement process, all relevant uncertainty components affecting the measurement result must be considered. Moreover, the specifications of the test characteristic must be known in order to establish the capability of the measuring system and in order to prove the capability of the measurement process. A measurement process capability analysis requires the estimation of the expanded measurement uncertainty UMP. The capability ratio QMP is used as an evaluation criterion. The value of the expanded measurement uncertainty UMP is available for consideration in decision rules for proving conformance or non-conformance according to ISO/TS 14253 Part 1 [13].

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Ongoing monitoring provides proof of the stability of a measuring instrument and shows long-term influences. The following sections describe the single procedures. 4.3

Specific Approaches

4.3.1

Measurement Errors

measured value

Measurement errors in a measurement process consist of known and unknown systematic errors from a number of different sources and causes. In German, the traditional term “measuring error” has been replaced by the term “measurement deviation” since the publication of DIN 1319:1995. In case of measuring instruments or measuring systems, the permissible systematic errors prescribed by different standards and guidelines (e.g. VDI/VDE/DGQ 2618 ff [28]) are referred to as maximum permissible error or error limit.

1 Outlier 2 Dispersion 1 3 Dispersion 2 4 Systematic error 1 5 Systematic error 2 6 True value

1 3

4

5

2

6 1 time

Figure 6:

Measurement errors in results of measurements [13]

Different types of measurement errors (see Figure 6) show up in measurement results: • random measurement errors Random errors are caused by non-controlled random influence factors. They may be characterized by the standard deviation and the type of distribution (see Dispersion 1 and 2 in Figure 6).

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• systematic measurement errors (known, unknown) Systematic errors (see Chapter 5.2.2) may be characterized by size and sign (+ or –): Bi = yi – true value (6) see Figure 6 The difference between the reference value of a measurement standard and the mean of the measured values often form the basis for calculating the systematic measurement error:

Bi = xg - xm xg

arithmetic

mean

of

the

measured

xm

reference value of the measurement standard

values

Where measurement errors are not regarded as systematic, the cause of the measurement error has not been sought for economic and complexity reason or the resolution is inadequate (e.g. %RE greater 5% of the specification; see Chapter 5.2.1). Remark:

Bias is not regarded as a constant but a random variable.

• instrumental drift Drift is caused by a systematic influence of non-controlled influence factors. It is often a time effect or a wear effect. Drift may be characterized by change per unit time or per amount of use. Instrumental drifts characterized by change per unit time must be recorded in a “long-term experiment” (over several days) prior to the first application of the measuring instrument and the drifts have to be considered in series production (e.g. in the form of an instruction: “switch on measuring instrument 20 minutes before use”). If required, instrumental drifts caused by wear effects must be assessed by reviewing the stability of the measuring instrument (e.g. control chart). • outlier Outliers are caused by non repeatable incidents in the measurement. Noise – electrical or mechanical (e.g. voltage peaks and vibrations) – may result in outliers. A frequent reason for outliers is human mistakes as reading and writing errors or mis-handling of measuring equipment. Outliers are impossible to characterize in advance, but they might occur in an experiment.

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Remark 1:

Frequently applied methods to determine the capability of measuring equipment include consideration of the systematic measurement error with regard to a measurement standard representing the true value. In many cases, however, the measurement standards used in production (working standards) are not identical to the test parts measured in series production. This could lead to unexpected measurement errors. In order to ensure that this errors are sufficiently minor, some representative test parts should be measured by means of a superior measurement procedure (e.g. prior to release). The results are compared and evaluated. The reproducibility of the measurement method is crucial.

Remark 2:

Production-related measuring instruments are often based on comparison measurements. Setting an instrument with the help of a working standard means correcting the systematic measurement error. A repeatability test using the same working standard normally leads to a smaller bias.

Remark 3:

Further measurement errors could occur in measurements at several measuring points and where different measuring systems or measurement procedures are used for one measurement task. In order to guarantee reproducible measurement results for all systems and procedures used, these errors must be analyzed in experiments.

4.3.2

Long-term Analysis of Measurement Process Capability

The known procedures for capability analyses and the capability of measuring systems and measurement processes are conducted over a period of several minutes up to several hours. However, the results are only “shortterm conclusions” and do not give any information about the long-term behaviour of the determined values. In order to gain profound information, the required inspections should be made several times over a reasonable, significant period. For further information about the estimation of uncertainty components see Table 14. 4.3.3

Reproducibility of Identical Measuring Systems

In many cases, several identical but independent measuring systems are used for measurement processes with the same measurement task. An alternative is to combine the identical, independent measuring systems into an overall measuring system for a specific measurement task. Each one of these individual measuring systems is regarded as separate measurement process. The aim of this analysis is to ensure the reproducibility of the single measuring systems by means of the variation and the measurement error. It is im-

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portant to inspect reference standards and parts. For further information about the estimation of uncertainty components see Table 14. 4.4

Standard Uncertainties

The GUM [22] “Guide to the expression of uncertainty in measurement” describes how to determine the measurement uncertainty specific to the respective measurement task. The standard uncertainties for every relevant influence factor are estimated using the mathematical model of the measurement process. Standard uncertainties quantify the single uncertainty components. According to the law of propagation of uncertainty, sensitivity coefficients are partial derivatives of the respective equation of the measurement model with regard to each single influence factor. An uncertainty budget summarizes standard uncertainties, associated sensitivity coefficients and the calculated combined and expanded measurement uncertainties. In the practice of industrial applied metrology, a special case of mathematical model (sum/difference or product/quotient) is assumed where the sensitivity coefficients equal “1“. This leads to a simple quadratic addition of the uncertainties (see Chapter 4.5). Remark:

Complex, technical interactions (such as wear, contamination, manufacturer’s specifications, form deviations, positioning accuracy, vibrations, etc.) that are hard to express mathematically are considered in the experiment in the form of a sum result.

The standard uncertainty •

u ( xi ) can be estimated by

the statistical evaluation of series of measurements Type A evaluation

or by •

the use of available information

Type B evaluation

The standard uncertainties estimated by means of the Type A and Type B evaluations are equal.

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4.4.1

Type A Evaluation (Standard Deviation)

In the simplest case, the standard deviation sg of n individual observations is calculated from a series of n observations obtained under the same specified conditions of measurement: n

sg =

∑ (x

- x)

2

i

i =1

n -1

In order to determine the standard deviation sg, n = 25 repeated measurements are recommended. This experiment is generally only conducted once in the estimation of measurement uncertainty. The standard deviation will be considered in the measurement uncertainty budget in the form of the standard measurement uncertainty u(xi) if, as is usual in practice, the measurement result is obtained in one measurement only.

u ( xi ) = sg A lower value for u(xi) is achieved by taking several repeated measurements with the sample size n∗ > 1

u ( xi ) =

sg n∗

as the standard measurement uncertainty of the mean of all the sample values (see Annex C). 4.4.2

Type A Evaluation (ANOVA)

In addition to the procedures described here for determining only one uncertainty component u(xi) of an influence factor, there is also a statistical technique used to identify and quantify the effects of several influence factors in an experiment. This procedure has been applied to capability analysis according to the MSA manual (Measurement Systems Analysis [1]) for years. In order to calculate the %GRR (Gage Repeatability & Reproducibility), the operator and equipment variation is estimated in an experiment (e.g. 3 operators measure each of 10 test parts twice: 3 · 10 · 2 = 60 measurements).

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In this case, the method of ANOVA (Analysis of Variance) is used as described Annex A. Remark:

The MSA manual [1] describes the method of ANOVA and the Average Range Method (ARM). Under statistical considerations, the method of ANOVA should be preferred to the ARM, the more so as the method of ANOVA also evaluates interactions. The method of ANOVA is indeed more complex in a mathematical sense, but the use of specific computer software makes its application easy.

In the same experiment, further influence factors, such as the uncertainty from test parts or different measuring systems can be evaluated, as is strongly recommended in Chapter 3.4.1 of the ISO/IEC Guide 98-3 (GUM) [22]. However, each additional influence factor increases the effort for this experiment considerably. In case of the example described above, the uncertainty from test part non-homogeneity could be determined by prompting each operator to measure each test part at four different measuring points twice. This would lead to 3 · 10 · 4 · 2 = 240 measurements. The required effort is economically not feasible. For this reason, the GUM [22] states: “This is rarely possible in practice due to limited time and resources”. There are two alternatives in order to minimize this effort: Reducing the number of experiments Design of experiments provides procedures for reducing the number of experiments without any major loss of information. It is recommended to use D-optimum experimental designs in the case of multistage factors. The estimation of variance components is based on the method of moments (ANOVA see Annex A.2). The corresponding experimental design can be created by suitable computer software automatically according to specified information about the experiment. Example for a D-optimum experimental design In order to estimate the standard uncertainty from the reproducibility of operators uAV, the uncertainty from the maximum value of repeatability or resolution uEV and from test part non-homogeneity uOBJ, 3 operators and 2 repeated measurements on each of 10 parts at each of 4 measuring points are required. This leads to 240 individual measurements. If a D-optimum design with a twofold interaction is created under the same conditions, the original 240 individual measurements can be reduced to 128 measure-

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ments. This almost halves the number of experiments. The example of Annex F.2 illustrates this option. Observation of a maximum of two influence factors If the example above only evaluates the influence of operators and equipment, the number of measurements is reduced. Alternatively, it is possible to evaluate two other influence factors (e.g. influence of test part and measuring instrument). Any other influence factor that is still missing is determined according to the Type A or Type B evaluation described above. Some variations might be included in several calculations. However, it is important not to consider them more than once in the evaluation of the measurement process. If, for example, the standard uncertainty uGV should be evaluated because of different measuring systems (e.g. micrometer), 1 operator can take 2 repeated measurements on each of 10 test parts from 3 identical measuring systems (1 · 10 · 2 · 3 = 60 measurements). In order to minimize the influence of the test parts, both repeated measurements should always be taken at the same measuring point. Thus, it is important to mark the measuring point used in the first measurement. 4.4.3

Type B Evaluation

If the standard uncertainty cannot be determined by the Type A evaluation or if this method is economically not feasible, the respective standard uncertainties are estimated based on available information. The pool of information may include: • previous measurement data • experience with or general knowledge of the behaviour and properties of relevant materials and instruments (similar or identical instruments) • manufacturer's specifications • data provided in calibration and other certificates • uncertainties assigned to reference data taken from handbooks • measured quantity values based on less than n = 10 measurements

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4.4.3.1

Type B Evaluation: Expanded Measurement Uncertainty UMP Known

If the available information provides numerical values for the expanded measurement uncertainty UMP and the used coverage factor k, e.g. from calibration certificates, the coverage factor k must be calculated as follows before multiplying it by the combined standard uncertainty u(y), see Chapter 4.6.

u ( xi ) =

4.4.3.2

UMP k

Type B Evaluation: Expanded Measurement Uncertainty UMP Unknown

If the expanded measurement uncertainty is unknown, a variation limit a or another upper or lower limit can be selected. The standard uncertainty u(xi) is calculated in consideration of the respective distribution type by transforming the limits of error. Table 3 contains typical distributions. Without any information about the distribution, the rectangular distribution is the safest alternative.

u( xi ) = a ⋅ b

where

a b

variation limit distribution factor

According to the International vocabulary of metrology [21], the maximum permissible measurement error is the maximum value of a measurement error relating to a known reference value. This reference value must be given in the specifications or regulations for a measurement, measuring instrument or a measuring system. The distribution factor depends on the respective distribution type (see Table 3). In estimating the standard uncertainty of the resolution of the measuring system, the rectangular distribution applies. If the range R is used as an estimator of the variation resulting from several repeated measurements (e.g. taken from a measurement standard), the distribution factor of the normal distribution (b = 0,5) is applied.

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Distribution type

Function (P = probability that the values lie within the interval ± a)

Distribution factor b

Standard uncertainty u(x)

0,5

u( xi ) = 0,5 ⋅ a

Normal distribution (Gaussian distribution)

-a

0

+a

(P = 95,45 %)

1

Rectangular distribution -a

0

u( xi ) =

3

+a

a

3

(P = 100 %)

Table 3:

4.5

Typical distribution types and associated variation limits for determining the standard uncertainty by the Type B evaluation

Combined Standard Uncertainty

In accordance with the mathematical model, the combined standard uncertainty u(y) is calculated from all standard uncertainty components obtained in the Type A and Type B evaluation. However, in the special cases described in Chapter 4.4 where the sensitivity coefficients equal “one”, the combined measurement uncertainty is calculated using quadratic addition:

u( y ) =

n

∑ u (x ) i =1

4.6

2

i

= u ( x1 ) + u ( x2 ) + u ( x3 ) + ... 2

2

2

Expanded Measurement Uncertainty

A measure of uncertainty with which the true value may vary from the measured value is termed expanded measurement uncertainty UMP. It is calculated by multiplying the combined measurement uncertainty by the coverage factor k (see Table 4):

UMP = k ⋅ u( y )

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The expanded measurement uncertainty UMP is calculated from a two-sided, limited probability density function of the combined measurement uncertainty based on a level of confidence of P = 1 − α = 0,9545 with an interval of α / 2 beyond the distribution quantiles. The special case of a symmetric distribution leads to the following calculation formula of the expanded measurement uncertainty: UMP = k ⋅ u(y ) and by assuming a normal distribution k = z1−α 2 = 2 . Assuming a normal distribution, the values and intervals of Table 4 apply. Coverage factor 1 2 3 Table 4:

Level of confidence 68,27% 95,45% 99,73%

Coverage factors

If the probability density function does not follow a normal distribution (e.g. in case of an asymmetric distribution), high levels of confidence, in particular, can lead to sharp deviations from the values listed above (see Annex D). Remark:

The level of confidence of 95,45% and the coverage factor k=2 is recommended for calculating the capability of measuring systems and measurement processes.

These assumptions allow for a statement about the probability that the true quantity value of the measurand yi lies within the interval. measurement result Y

y i - UMP ,..., y i + UMP

UM P

U MP

measurement value yi

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4.7

Calculation of Capability Ratios

When inspecting by variables (measuring), the capability of a measurement process is established by determining the expanded measurement uncertainty specific to the respective measurement task in consideration of each dominant influence factor (see Chapter 4.1). The characteristics and specifications to be tested must be determined before the inspection starts. Figure 7 shows a flow chart for assessing the capability of measuring systems or measurement processes. In case of inspections by attributes (gauging), special analyses are required in order to establish the capability of measurement processes (see Chapter 9).

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Figure 7:

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The capability ratios QMS for the measuring system and QMP for the measurement process help to evaluate metrological demands on the measuring system or measurement process. They are defined as capability ratios and expressed as percentages.

QMS =

2 ⋅ UMS TOL

⋅ 100%

or

QMP =

2 ⋅ UMP TOL

⋅ 100%

The capability ratios are associated with the respective limits QMS_max or QMP_max. If it is demonstrated that the capability ratios QMS < QMS_max

or

QMP < QMP_max,

do not exceed these limits, the capability of the measuring system or measurement process is established.

Remark:

According to ISO/TS 14253 [13], the tolerance zone is reduced on either side by the expanded measurement uncertainty UMP. For this reason, the ratio of 2·UMP is used as the tolerance TOL for the capability ratio.

-UMP +UMP

-UMP +UMP

2·UM P

L Figure 8:

U

Illustration of a capability ratio

The limits for the capability of measuring systems and measurements processes must be determined. It is important to consider that the influences of the form deviation of test parts can affect the evaluation of the measurement process considerably. It is recommended that the capability ratio for measuring systems, QMS_max amounts to 15% and, for measurement processes, QMP_max amounts to 30%.

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Remark 1:

The proposed limits serve as guide values that cannot be generalized in any case. In individual cases, the limits must be agreed upon between supplier and customer. If the proposed limits are unrealistic, individual agreements must be made depending on the respective characteristic and its specifications (wide or narrow/very narrow tolerances). It is important always to take into account the entire measurement process. In order to determine the limits, it is necessary to consider the economic and technical requirements. For this reasons, the limits should be as wide as possible and as narrow as necessary.

Remark 2:

If the capability of the production process reaches a sufficiently high value (e.g. Cp, Cpk ≥ 2,0) that was established by an adequate measurement process, a separate observation of the expanded measurement uncertainty at the specification limits is not required because the evaluation of the process already includes the variation of the measurement process.

The capability ratio QMP corresponds to the percentage by which the tolerance zone of the test characteristic is reduced or extended according to ISO/TS 14253 Part 1 [13]. Chapter 4.10 illustrates the relation between the observed capability index and the real capability index in case of a twosided tolerance zone for various QMP values. As shown in Figure 9 and Table 6, the effects can be significant. Remark:

Determination of the uncertainty components of the measuring system is not required when the MPE has been proved and documented:

uMS = MPE

3

If more than one MPE value affects the combined standard uncertainty of the measuring system. the following formula applies:

u

2 MS

MPE12 MPE 2 2 = + + ... 3 3

uMS =

MPE12 MPE22 + + ... 3 3

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4.8

Minimum Possible Tolerance for Measuring Systems / Measurement Processes

In order to classify measuring systems and measurement processes, it is advisable to calculate the minimum tolerance required to establish the capability of the measuring system and the measurement process. This tolerance is calculated by rearranging the formula and replacing QMS or QMP by QMS_max or QMP_max. The result will be the minimum possible tolerance for the measuring system TOLMIN-UMS or the measurement process TOLMIN-UMP:

TOLMIN -UMS =

2 ⋅ UMS ⋅ 100% QMS _ max

TOLMIN -UMP =

2 ⋅ UMP ⋅ 100% QMP _ max

or

The inspected measurement process can be used down to the minimum tolerance value of TOLMIN-UMP. Remark 1:

If the minimum tolerance value TOLMIN-UMS for the measuring system is already similar to the specified tolerance TOL, an estimation of the standard uncertainties of the measurement process is unnecessary because the result would exceed the QMP_max value anyway, unless the uncertainties are negligibly small.

Remark 2:

This procedure is very useful in case of standard measuring instruments and similar measurement tasks.

Remark 3:

The calculated minimum tolerance only applies to the respective measurement task.

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4.9

Uncertainty Budget

An uncertainty budget gives a clear overview of the capability of measuring systems and measurement processes. Table 5 shows an example of a possible uncertainty budget. Evaluation type

u(xi)

A/B

name u(xi)

A

...

...

...

Variation limit a

Coverage factor b

name u(xi)

B

u ( xi ) = a ⋅ b

Type B evaluation

Standard deviation or Ui from ANOVA

Uncertainty component (value)

Uncertainty component (name)

Type A evaluation

u(xi)

u ( x i ) = si or Ui from ANOVA

...

...

...

...

Combined measurement uncertainty

u( y ) =

n

∑ u( x )

2

i

i =1

Expanded measurement uncertainty

Table 5:

UMS = k ⋅ u( y ) UMP = k ⋅ u( y )

Information provided by an uncertainty budget

Every measured quantity value obtained in a measurement in practice includes the expanded measurement uncertainty UMP.

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4.10

Capability of the Measurement and Production Processes

Figure 9 displays the relation between observed process capability index (Cp;obs), the real process capability index (Cp;real) and the capability ratio (QMP).

Q MP

50%

40%

30%

4,00 3,80

real C value

3,60 3,40 3,20 3,00 2,80 2,60 2,40 2,20

1,72

20% 10%

2,00 1,80 1,60 1,40 1,20 1,00 0,80 0,60 0,40

0,20 0,00 0,50 0,60 0,70 0,80 0,90 1,00 1,10 1,20 1,30 1,40 1,50 1,60 1,70 1,80 1,90 2,00 1,33 1,67

observed C value

Figure 9:

Display of the real C-value as a function of the observed C-value subject to QMP

The curve shape displayed in Figure 9 shows that a real capability index of 2,21 from an actual production process and a measurement capability figure QMP = 40% only results in an observed capability index of 1,33. A capability ratio QMP of 10% shows to a considerably better result. In this case, an observed C-value of 1,67 corresponds to a real C-value of 1,72.

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The calculation is based on the following assumptions: •

Measured quantity values of the manufactured characteristic are normally distributed.

•

The calculation of the Cp index is based on 99,73% reference value estimated by 6 standard deviations.

•

The observed, empirical standard deviation is: sobs =

•

The uncertainty range regarding the specification limits is symmetrical.

2 2 sreal + sMP

• The coverage factor used to calculate the combined uncertainty is 2. Based on the curve shapes (Figure 9), the Cp;real and Cp;obj values can be specified for typical C-values as a function of QMS (Table 6). Real C-value for the process when… Observed Cvalue

QMP = 10%

QMP = 20%

QMP = 30%

QMP = 40%

QMP = 50%

0,67

0,67

0,68

0,70

0,73

0,77

1,00

1,01

1,05

1,12

1,25

1,51

1,33

1,36

1,45

1,66

2,21

18,82

1,67

1,72

1,93

2,53

2,00

2,10

2,50

4,59

Table 6:

4.11

Relation between Cp;real and Cp;obs for typical C-values

Dealing with Not Capable Measuring Systems / Measurement Processes

In order to improve a measuring system / measurement process, the standard uncertainties must be reduced, for example, •

by using measurement procedures including a lower measurement uncertainty and

•

by reducing the effects of the influence factors affecting the measurement process (see Figure 5).

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The application of measurement procedures resulting in a lower measurement uncertainty is a simple solution, however, they must be proved economically optimal for performing the measuring task. Here are some examples of how to reduce the effects of influence factors on the measurement uncertainty: Measuring equipment / material measure • selecting more suitable sensors • selecting material measures of a higher quality • selecting a sampling strategy • optimizing the sampling strategy (e.g. measuring speed, definition of measuring points, mounting device, settings, algorithms for evaluation, sequence) • repeated measurements including averaging (Annex C) Test parts • correcting temperature of a test part to a standard temperature of 20° C • cleanliness • improving dimensional stability and surface properties • avoiding burrs Operator • improving skills and qualifications of operators • taking measures to raise employee motivation Environment (temperature, vibrations, etc.) • avoiding negative influences by selecting proper workstation or screen • taking measurements under temperature-controlled conditions • positioning measuring instruments in a place where they are protected against vibrations Stability of a measuring instrument (stability) • detecting and correcting components causing a trend

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5

Measurement Process Capability Analysis

5.1

Basic Principles

The previous chapters dealt with the following, general topics: • necessity to determine the expanded measurement uncertainty UMS for a measuring system and UMP for a measurement process • calculation of the expanded measurement uncertainties UMS and UMP based on the combined measurement uncertainty uMS or uMP and the coverage factor k • criteria for the capability ratios of measuring systems QMS and measurement processes QMP • schematic approach for proving the capability of a measuring system and measurement process This chapter explains how to determine the individual uncertainty components u(xi) either by using the Type B evaluation (see Chapter 4.4) or by experiment (see Type A evaluation, Chapter 4.4). For this purpose, a standardized method is available and recommended covering a large part of measurement uncertainty estimations that occur in practice. In some cases, where the preconditions set out for this method are not present, the user must use the general current method for determining the measurement uncertainty that is described in the “Guide to the expression of uncertainty in measurement“ (ISO/IEC Guide 98-3 [22]). If the uncertainty components estimated from an experiment do not correspond to the expected spread of these components in the actual measurement process, then these components must not be estimated experimentally. Instead, they should be derived using a mathematical model (e.g. constant temperature in a measuring laboratory when conducting a test and the normal temperature variations of the place of the future application). In this model, the expected variation in the real measurement process must be considered. The following chapters, however, are based on the assumption that only the uncertainty components test part homogeneity, resolution and temperature should be derived using a mathematical model.

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5.2

Capability Analysis of a Measuring System

In principle, the expanded measurement uncertainty refers to the entire measurement process (see Chapter 4.6). However, since the measuring system is an essential part of the measurement process, it should be evaluated separately. Its capability ratio QMS (see Chapter 5.2.1) is generally easier to determine than the capability of the measurement process. Measuring systems require that the resolution (%RE) should be lower than 5% of the specification. If this requirement is not satisfied, a different measuring system has to be applied. Uncertainty components related to the measuring system are “calibration uncertainty on the reference standard“, “uncertainty from bias,”, “uncertainty from measurement repeatability” and “uncertainty from linearity” (see Table 7). The standard uncertainty due to the calibration on the reference standard is given in the calibration certificate. If the bias is not compensated by calculation, repeated measurements are taken on one, two or three measurement standards, depending on the measuring system and measurement task. The values of the standards are approximately equidistantly placed throughout the relevant measuring interval associated with the measurement process (see Figure 14). The measured quantity values form the basis of determining the standard uncertainties due to the bias and equipment influences. Before starting the analysis, the working point(s) of the measuring system must be set accordingly. For further information, see Annex E. If the bias of the measuring system can be corrected, the regression function has to be determined by ANOVA (see Chapter 5.2.2). In this case, repeated measurements are taken on at least three measurement standards whose values are placed throughout the relevant measuring interval (see Figure 14). These measured values are used to calculate the regression function and the compensation is made. In spite of the compensation, some uncertainties are remaining. They are composed of the pure error standard deviation uEV and the lack-of-fit uLIN. Both must be considered in calculating the combined standard uncertainty of the measuring system. Figure 10 shows a flow chart of the measuring system capability analysis. Table 7 explains how to determine single standard uncertainties. Chapter 4.7 describes how to calculate the capability ratio QMS or the minimum permissible tolerance TOLMIN-UMS.

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Measurement System Capability

%RE

no

5%TOL

Use measurement system with a sufficiently high resolution

yes yes

MPE known and accepted? no

yes

no Linearity uLIN known?

Prepare trial

Prepare trial

minimum 3 reference standards, repeat measurements

1, 2, or 3 reference standards, repeat measurements

UCALi uCAL = max 2

UCALi uCAL = max 2

document MPE

{ }

from ANOVA:

uEVR = max sgi

uEVR (pure error deviation)

Bi uBi = max i 3

from ANOVA: uLin (lack-of-fit deviation)

uMS see table 12

UMS = k ⋅ uMS

Measurement system capable

QMS =

yes

2 ⋅ UMS ⋅ 100% TOL

QMS

QMS_max

TOLMIN -UMS =

no

2 ⋅ UMS ⋅ 100% QMS _ max

Measurement system not capable

Figure 10: Measuring system capability analysis

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Uncertainty components

Symbol

Test / model %RE must be lower/equal than 5% of the specification

Resolution of the measuring system

uRE

Calibration uncertainty

uCAL

Repeatability on reference standard

uEVR

Uncertainty from bias

uBI

1 RE 1 ⋅ = ⋅ RE where RE is the resolution 3 2 12 See note on page 56. Obtained from the calibration certificate of measurement standards. In cases where the uncertainty in protocol is given as an expanded uncertainty, it should be divided by the corresponding coverage factor: uCAL = UCAL / kCAL Depending on the measuring system, repeated measurements are taken on one, two or three standards. On one measurement standard, at least 25 repeated measurements are taken whereby their spread uEVR =sg can be estimated. On each of two standards, at least 15 repeated measurements are taken whereby their spread uEVR can be estimated. The greatest one of the results is used. On each of three standards, at least 10 repeated measurements are taken whereby their spread uEVR can be estimated. The greatest one of the results is used. From the measured values on a reference standard taken during a repeatability analysis, the standard uncertainty uBI can be calculated based on the systematic measurement error from: uRE =

uBI =

xg - xm 3

In case of two or three measurement standards, the greatest one of the results is used.

Uncertainty from linearity

uLIN

Uncertainty from other inuMS_REST fluence components Table 7:

52

In the calculation of linearity, uLIN is determined by the method of ANOVA (lack-of-fit deviation / see Annex A.2). For measuring systems with linear material measure, the uncertainty from linearity is determined based on the results from the manufacturer’s or calibration certificate. Any further influences on the measuring system, supposed or substantial, must be estimated separately by experiments or from tables and manufacturer’s specifications.

Typical uncertainty components of a measuring system

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Remark:

The ISO/TS 15530 [16] adds the bias BI as a whole to the other components in order to calculate the combined standard uncertainty for the measuring system uMS:

uMS =

(u

2 CAL

2 + uEVR ) + Bi

It is assumed that Bi is generally small. If the bias is large, it must be corrected on the measuring system. In order not to make a general decision, this document treats the standard uncertainty arising from the bias as any other standard uncertainty component:

uMS =

(u

2 CAL

2 + uEVR + uBI2 )

In order to make the two formulas comparable, only the uCAL, uEVR and uBi components were observed.

The estimation of each single uncertainty component is not required when the maximum permissible error MPE of the measuring system is known, traceable and documented. In this case, uMS is determined by MPE ( uMS = MPE 3 ). However, calculations referring to characteristics require these estimations. The following chapters explain how to determine the respective standard uncertainty. 5.2.1

Resolution of the Measuring System

In order to establish the capability of a measuring system, its resolution (see Table 7) must not exceed 5 % of the specification. For this reason, the

standard uncertainty arising from the resolution is only considered for measurement processes. RE is the smallest step (between two scale marks) of an analogue measuring instrument that can be read clearly or the step in last digit of a digital display (e.g. 0,001, 0,5 or 1,0). 5.2.2

Repeatability, Systematic Measurement Error, Linearity

In industrial practice, the reported uncertainty of the measuring system is usually limited to the calibration uncertainty on the used reference standard, the uncertainty from repeatability and from measurement bias. Dokument wurde bereitgestellt vom VDA-QMC Internetportal am 18.08.2011 um 01:03

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In order to determine the uncertainty arising from the repeatability on a measurement standard, it is recommended to use the experiment known as a “Type 1 study”, used for determining the measuring system capability indices Cg or Cgk (see Chapter 5.2.2.1 and [25]). This study can also be applied to two or three standards. If the linearity of the measuring system has to be determined, it can be done by means of a linearity study based on at least three reference standards. The result of this investigation (regression function) can then be used for correction of the measurement result which reduces the uncertainty from linearity.(see Chapter 5.2.2.2).

5.2.2.1

Estimating the Systematic Measurement Error and Repeatability according to the “Type 1 Study”

The systematic measurement error (bias) must be reduced as far as possible by adjustment or calculation. Nevertheless, some small or unknown residual systematic errors will remain. The errors are the maximum values of the known systematic measurement errors within the used measuring interval and cannot be corrected. This error can be estimated by an investigation on a measurement standard (material measure). This study can also be applied with several standards. Repeated measurements on a standard In order to determine the uncertainty from repeatability and resolution on a reference standard uEVR, it is recommended to use the experiment known as a “Type 1 study” (determining the capability of the measuring system) (see guide to the proof of measuring system capability [25]). However, in this case, the aim of the experiment is the estimation of uncertainty components rather than the estimation of the capability ratio. The determination of the uncertainty uEVR comes from the standard deviation of the repeatability sg estimated from measurements on a measurement standard. It should be based on the spread of a minimum of 25 repeated measurements, to estimate the combined effect of bias and repeatability.

uEVR = sg =

54

K 1 ⋅ ∑ y i - xg K - 1 i =1

(

)

2

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where:

K number of repeated measurements, normally K = 25 or more yi single value of the i-th measurement x g the arithmetic mean of all the sample values

The standard uncertainty uBI from bias is calculated from:

uBI = where:

xm

xg - x m 3

reference quantity value of the measurement standard within the tolerance of the test characteristic and bias Bi

Bi = xg - xm The capability indices Cg and Cgk used in [26] are calculated from the series of measurements determined thereby:

Cg =

0,2 ⋅ TOL 4 ⋅ sg

Cgk =

0,1⋅ TOL - Bi 2 ⋅ sg

If uCAL and uBI are neglected, QMS can be compared to Cg. In this case, a Cg-value of 1,33 corresponds to a QMS_max -value of 15 % (see Chapter 4.7). Remark:

There are several company guidelines using a sample standard deviation of 6sg or 3sg (coverage probability P = 99,73%) instead of 4sg of 2sg (P = 95,45%). In this case, a Cg-value of 1,33 corresponds to a QMS_max-value of 10% (see Chapter 4.7).

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The comparison between the presented determination of standard uncertainties and calculation of capability indices shows that the procedure, in order to obtain measured quantity values, is the same. The difference lies in the derived statistical values: •

uEVR and uBI

•

Cg

(measurement uncertainty)

and Cgk (capability of measuring system)

and in the interpretation of results. In this way, available measured quantity values gained in previous capability analyses according to the „Type 1 study” for determining the standard uncertainties can be used. Remark:

The result of uEVR can be compared to uRE. The greater value of the two is used as the standard uncertainty from repeatability uEV. Reason: Even though the requirement %RE ≤ 5% is satisfied, it is possible that, for example in case of 25 repeated measurements on a reference standard, the variation may be zero (uEVR =0) or only one value differs in its resolution from the other values of a series of measurements. In this case, it generally applies uEVR < uRE. Example: A diameter of 20 ± 0,2 mm is to be inspected. A digital micrometer with a resolution of 0,01 mm (%RE = 2,5 %) meets the requirement %RE ≤ 5%. If this micrometer performs 25 repeated measurements on a gauge block (20 mm), a value of 20,00 is frequently obtained. This leads to an uncertainty uEVR amounting to zero. In this case, the standard uncertainty from the resolution of the measuring system uRE = 2,89 µm must be used rather than the standard uncertainty from repeatability.

Example on one measurement standard In this example, a characteristic with a nominal quantity value of 6 mm is used. The upper specification limit is U = 6,03 mm and the lower specification limit is L = 5,97 mm. This leads to a specification of 0,06 mm. The uncertainty from linearity is negligibly small (uLIN = 0). The resolution of the used measuring system amounts to 0,001 mm (≙%RE = 1,66%). Thus, the requirement %RE ≤ 5% is fulfilled. The calibration certificate for the reference standard with a reference quantity value of 6,002 mm gives UCAL= 0,002 mm and kCAL= 2. In this example, 50 repeated measurements (25 would be sufficient) are performed on the reference standard (see Table 8).

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Table 8:

Measured values of the repeated measurements on the standard

From these data and measured quantity values, the following standard uncertainties and results of the measuring system are obtained:

Figure 11: Standard uncertainties of the measuring system

Figure 12: Results of the measuring system The measuring system is applicable down to a minimum tolerance of 0,042 mm.

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Remark:

The results are based on a statistical evaluation whose informational value must be assessed by means of the confidence interval. However, this is not done in this example. Thus, a repetition of the experiment or different sample sizes leads to slightly different results.

Repeated measurements on two measurement standards For this analysis, the use of a material measure is recommended whose actual values lie within a range of ± 10% around the specification limits (see Figure 13). Before starting the study, the measuring system must be set according to the procedure described in Annex E.

-10%

+10%

-10%

xml

+10%

xmu

L

U

lower tolerance limit

upper tolerance limit

Figure 13: Recommended location of the material measure xml actual value of material measure near the lower specification limit L xmu actual value of material measure near the upper specification limit U In general, a minimum of 15 repeated measurements should be performed on each measurement standard. Based on these measurement results, uEVR and uBI are estimated for each measurement standard according to the described procedure associated with standards. The greater value of the two serves as the uncertainty component uEVR or uBI.

UEVR = max UBI = max

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Repeated measurements on three measurement standards For this simplified linearity analysis, the use of a material measure is recommended whose actual values lie within a range of ± 10% around the specification limits (Figure 14).

-10%

+10%

xml

-10%

+10%

-10%

xmm

L

+10%

xmu U

Figure 14: Recommended location of the material measure

xml actual value of material measure near the lower specification limit L xmm actual value of material measure near the center of the specification xmu actual value of material measure near the upper specification limit U In general, a minimum of 10 repeated measurements should be performed on each measurement standard. Based on these measurement results, uEVR and uBI are estimated for each measurement standard according to the described procedure associated with standards. The greater value serves as the uncertainty component uEVR or uBI.

UEVR = max. {uEVR1, uEVR2, uEVR3} UBI = max. {uBI1, uBI2, uBI3} In this case, the standard uncertainty from linearity is part of UBI. This leads to ULIN = 0.

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5.2.2.2

Linearity Analysis with Correction on the Measuring Instrument

The following procedure is suggested: • On each of a minimum of three reference standards at least 10 repeated measurements are performed (the number of standards multiplied by the number of repeated measurements must lead to a minimal sample size of 30). • The reference standards should be evenly spread over the entire specification zone. The areas associated with the specification limits displayed in Figure 13 must be considered. • A regression analysis is performed in order to estimate the linear regression function by assuming that the pure error standard deviation is constant over the spread of measurement results (see Figure 15 and Annex A.1). • An analysis of variance is performed whereby residuals are analyzed due to a lack-of-fit and pure error standard deviation (see Figure 15 and Annex A.2). • Estimation of the uncertainty components based on the results of the method of ANOVA. • Correction on the measuring system, i.e. correction on future measurements (where appropriate). Generally, the following preconditions apply: • The pure error standard deviation (standard deviations from repeated measurements on the standards) is always constant. • The regression function is linear (regression line). • The calibration uncertainty on the reference standards is lower than 5 % of the specification. • The measurements are representative of the future use of the measuring system regarding the environment and other conditions. • The repeated measurements of the reference standards are independent from each other and the results are normally distributed. • The values of the standards are approximately equidistantly placed throughout the relevant measuring interval.

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Example of a linearity analysis with regression analysis For a better illustration of this issue, the example includes a high lack-of-fit and a considerable pure error standard deviation. This leads to great uncertainties in the end. Moreover, more than three reference standards are used. This is quite unusual in practice. In a linearity analysis, 5 repeated measurements (K=5) on each of 6 reference standards (N=6) are performed. The minimum requirement of a sample size of N ⋅ K =30 is satisfied. The following values (in mm) were determined:

Table 9:

Measured quantity values of the analysis

Assuming that the preconditions listed in Chapter 5.2.2.2 are fulfilled, the regression function is calculated from the reference quantity values xn and the measured quantity values ynk. Annex A.1 contains the formulas for estimating the unknown parameters of the function. regression function:

yˆ = -0,6176 + 0,9183 ⋅ x

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Figure 15: Diagram of an analysis of variance Figure 15 displays relevant components of the regression function and the analysis of variance and their relation to one another. The diagram gives an initial impression regarding the following information: • whether the measurement process is under statistical control during the experiment • correctness of assuming a constant linearity (lack-of-fit) • deviation of the measured quantity values from the regression line (residuals) • deviation of the single repeated measurements on a reference standard (pure error standard deviation) • the presence of outliers that need further investigation

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For an evaluation, the residuals enk can be observed in a value chart (see Figure 16 a)). In order to find out whether the single measurements are independent from one another, the residuals enk must be normally distributed. This can be seen by b) plotting them on probability plot (see Figure 16). Here, the measured quantity values should adapt to the probability straight line as far as possible. The spread of the residuals enk can be obtained by c) plotting them on the fitted values yˆn (see Figure 16).

a.)

b.)

c.)

Figure 16: a.) Value chart of the residuals b.) Residuals plotted on a probability plot c.) Residuals plotted on fitted values

If there are inconsistencies in the graphical display, they must be eliminated. If necessary, the analysis must be repeated. After the graphical evaluation of the regression function and the residuals, the estimates of the uncertainty components uLIN and uEVR should be calculated by using the method of ANOVA. Annex A.2 provides the required ANOVA table with the associated formulas.

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A given calibration uncertainty of uCAL = 0,05, a resolution of RE = 0,001 mm and a tolerance of TOL = 30 mm lead to the following results:

Figure 17: Uncertainty budget of the measuring system

Figure 18: Result for the measuring system Due to the sharp linearity deviation and repeatability, the measuring system is not qualified for the measurement task. A qualified measuring system requires a minimum tolerance of 251 mm.

5.3

Measurement Process Capability Analysis

In addition to the uncertainty components of the measuring systems described above, further uncertainty components must be determined in order to evaluate the measurement process under real conditions. The procedure displayed in Figure 19 is recommended in order to perform a measurement process capability analysis.

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Table 10 and 11 contain the single standard uncertainties and Table 14 explains how to estimate or calculate the respective standard uncertainty. Table 12 gives an overview of how to calculate the expanded measurement uncertainty of the measuring system UMS and the measurement process UMP. It also contains the capability ratios for the measuring system QMS and the measurement process QMP. By comparing these results to a specified limit, it is possible to determine whether the respective measuring system or measurement process is qualified for the intended measurement task. If the ratio exceeds or goes below the specified limit, the following questions can be answered by rearranging the stated equation. •

Statistic exceeds limit: “What is the minimum tolerance demanded in order, just barely, to achieve capability? “

•

Statistic goes below limit: “What is the maximum tolerance demanded in order, just barely, to achieve capability? “

This requires the calculation of the statistics for the measuring system TOLMIN-UMS and the measurement process TOLMIN-UMP.

Uncertainty components Repeatability on test parts Reproducibility of operators Reproducibility of measuring systems (place of measurement) Reproducibility over time Uncertainty from interaction(s)

Table 10:

Symbol uEVO uAV

uGV uSTAB uIAi

Test / model Minimum sample size: 30 Always a minimum of 2 repeated measurements on a minimum of 3 test parts measured by a minimum of 2 operators (if relevant), measured by a minimum of 2 different measuring systems (if relevant) see “Type 2 study” MSA [1] Estimation of uncertainty components by the method of ANOVA.

Typical uncertainty components of the measurement process determined in experiments (Type A evaluation)

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{

}

2 2 2 2 2 uMP = uCAL + max uEVO , uEVR , uRE + uBI2 + uLI2 N 2 2 2 2 2 2 +u AV + uGV + uSTAB + uT2 + uOBJ + ∑ uIAi + uRES T

UMP = k ⋅ uMP

QMP =

2 ⋅ UMP ⋅ 100% TOL

TOLMIN -UMP =

2 ⋅ UMP ⋅ 100% QMP _ max

Figure 19: Measurement process capability analysis

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Uncertainty Symbol components Uncertainty caused by test part nonuOBJ homogeneity

Model

uOBJ =

aOBJ

where aOBJ is the maximum form deviation (see Table 14)

3

The influence caused by temperature can be calculated using the formula: 2 2 uT = uTD + uTA

uTD uTA

where

uncertainty caused by temperature differences uncertainty caused by expansion coefficients

The uncertainty caused by temperature differences could e.g. be estimated in compliance with ISO/TR 14253 Part 2 [15]:

uTD = ∆T ⋅ α ⋅ l ⋅ α ∆T l

uT

1

where

3

expansion coefficient temperature difference observed value for length measurement

If a measuring instrument is set using one reference part and the test part and reference part have different temperatures and expansion coefficients, uTD can be calculated from the difference ∆l of the expansion between test part and the working standard:

Uncertainty caused by temperature

uTD = ∆l ⋅

1 3

The uncertainty on expansion coefficients could e.g. be estimated in compliance with ISO/TR 15530-3 [16]:

uTA = T - 20 °C ⋅ uα ⋅ l where average temperature during the measurement uncertainty on the coefficient of expansion l observed value for length measurement alternatively: see Annex C1, uncertainty with correction of the different linear expansions see Annex C2, uncertainty without correction of the different linear expansions Any further influences of the measurement process must be estimated separately. T

uα

Uncertainty caused by other influence comuREST ponents

Table 11:

Typical uncertainty components of the measurement process from available information (Type B evaluation)

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Table 12 gives an overview of the calculation of the combined measurement uncertainty, the expanded measurement uncertainty and the capability ratios or the minimum tolerance of the measuring system and the measurement process. Uncertainty components

Symbol

Calibration uncertainty on standard

uCAL

Uncertainty from bias

uBI

Uncertainty from linearity

uLIN

Repeatability on standards

uEVR

Uncertainty from other influence components (measuring system)

uMS_REST

Combined measurement uncertainties

Expanded measurement uncertainties

u MS = 2 uCAL

{

+u + u 2 BI

2 LIN

+u

QMS =

}

2 2 + max u EVR , uRE

2 MS _ REST

U MS = k ⋅ uMS

or

MPE

2

TOL

⋅ 100%

TMIN −UMS =

or

Maximum permissible error

MPE

2 1

MPE 3

Repeatability on test part Reproducibility of operators Reproducibility of measuring systems Reproducibility over time Uncertainty from interaction(s) Uncertainty from test part inhomogeneity Resolution of the measuring system Uncertainty from temperature Uncertainty from other influence components

68

2 ⋅ UMS

2 ⋅ U MS ⋅ 100% QMS_max

3

Table 12:

Capability ratio minimum tolerance

+

MPE22 … 3

uEVO uAV

uMP =

uGV

QMP =

uSTAB

u

uIAi

+ max u

uOBJ uRE uT

2 CAL

{

2 EVR

2 EVO

, u

2 RE

, u

}

2 +uBI2 + uLIN

U MP = k ⋅ u MP

2 2 2 2 +uAV + uGV + uSTAB + uOBJ 2 +uT2 + uREST + ∑ uIA2

2 ⋅ U MP TOL

⋅ 100%

TMIN −UMP = 2 ⋅ UMP ⋅100% QMP_max

i

i

uREST

Calculation of the expanded measurement uncertainty of the measuring system / measurement process and their capability

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5.3.1

Example for Determining the Uncertainty Components of the Measurement Process

In order to determine the capability of a measurement process, the standard uncertainties of the measuring system were estimated (see example with one standard in Chapter 5.2.2.1) and an experiment was conducted by 3 operators performing 2 repeated measurements on each of 10 test parts. The results were evaluated by means of the method of ANOVA (see MSA [1]). Table 13 lists the measured quantity values leading to the standard uncertainties shown in Figure 20 and the results displayed in Figure 21. Since the interactions between operator and part is not significant, pooling is used in the calculation according to the method of ANOVA (see Annex A.2).

Table 13:

Remark:

Measured quantity values taken in 2 repeated measurements on 10 parts by 3 operators

According to MSA [1], the statistical value %GR & R EV 2 AV 2 is calculated from the measured quantity values by using the same calculation method of ANOVA. In this case EV=uEVO and AV=uAV. This example again shows the similarities between MSA and VDA 5. The difference does not lie in the procedure, but in the different statistics and interpretations.

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Figure 20: Standard uncertainties of the measurement process

Figure 21: Results of the measurement process The measurement process is applicable down to a minimum tolerance of 0,03 mm (rounded figure).

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6

Ongoing Review of the Measurement Process Capability

6.1

General Review of the Measurement Stability

The short-term as well as the long-term stability has to be taken into account when the capability of the measurement process is calculated. However, a change in bias caused by drift, unintentional damage or new additional uncertainty components, which were not known by the time of calculation of the capability, can change the bias in the measurement process over time so that capability is not established anymore. A control chart should be used to be able to determine those possible significant changes in the measurement process. The following sequence is recommended:

Step 1: Select an appropriate reference standard (working standard) or calibrated work piece with a known value for the test characteristic. Step 2: Carry out regular measurements on the reference standard (working standard) or test part (e.g. every day in a working week or at the beginning / end of a shift or prior to each measurement in case of a measurement process used only rarely). Step 3: Plot the measured values on a control chart. Remark:

The action limits, are calculated in accordance with known methods of quality control charting techniques.

Step 4: Case 1 If no out of control signal is detected, it is assumed that the measurement process has not changed significantly. Case 2 If an out of control signal is detected, the measurement process is assumed to have changed and shall be reviewed. With this approach, the measurement process is continuously monitored and significant changes can be detected. The resulting knowledge about the measurement process can be taken into account when determining the qualification interval for calibration.

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6.2

Correcting the Regression Function

If there was doubt about the linearity of the measuring system during the calculation and if a regression function has been experimentally determined, the method given here can be used for the ongoing review of the linearity of the measuring system. A control chart gives a signal when the regression function needs to be updated.

Step 1: Calculating control limits with figures found in Chapter 5.2.2.2 σˆ ⋅ t α ⋅ (N ⋅ K - 2) βˆ1 (1- 2⋅m )

upper control limit:

UCL =

lower control limit:

LCL = -

σˆ ⋅ t α ⋅ (N ⋅ K - 2 ) βˆ1 (1- 2⋅m )

Step 2: Selecting the m reference standards The reference standards (minimum 2) must be chosen in a way that their nominal values cover the range of observations that occur under the actual production conditions. Step 3: Repeating measurements on the reference standards For example, the reference standards should be measured every day in a working week.

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Step 4: Transforming the p measurement values on the m standards The p values of the m standards are transformed with the help of the regression function:

x=

y − β0

β1

Then each of the differences between the "true" and the transformed values is calculated. Step 5: Plotting the differences on a control chart The differences calculated in Step 4 are plotted on the time axis. Step 6: Deciding the validity of the regression function This decision will depend on whether all the differences of all standards are within the control limits.

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7

Practical Guidance to Determining Typical Standard Uncertainties

Table 14 gives notes and suggestions together with the associated references about how to determine the standard uncertainties from the respective influence factor.

Source of uncertainty Resolution of the measuring system uRE

Suggestions / remarks RE= is the smallest step (between two scale marks) of an analogue measuring instrument or the step in last digit (e.g. 0,1/0,5/1,0) of a digital display. The resolution should be much lower than the specification interval for the test part to be measured (e.g. %RE ≤ 5% of the specification interval). In this case, the resolution is included in the repeatability.

Type A/B B

Reference Reading / estimations or manufacturer’s specification

Calculate the standard uncertainty from resolution using the formula: uRE =

Calibration uncertainty on the standard uCAL

1 RE 1 ⋅ = ⋅ RE 3 2 12

In metrology, a coverage factor of k=2 is typically used in calculations. The standard uncertainty uCAL is calculated by dividing the expanded uncertainty UCAL by the coverage factor 2. The respective K-value is taken from the calibration certificate.

B

Calibration certificate / manufacturer’s specification / internal calibration

Remark The calibration uncertainty shall be much lower than the expected measurement uncertainty.

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Source of uncertainty

Suggestions / remarks

Repeatability uEVR (on standard ) and estimation of the uncertainty from bias UBI

The uncertainty components can be determined experimentally. Before using a measuring system, it must normally be set using one or two standards. The deviations from the reference quantity value determined by calibration must be considered. Remark Measurement on one reference standard In general, at least 25 repeated measurements on one standard are performed. The standard must be clamped, released, and always measured in the same place of measurement (when the influence of the standard shall not be considered). Determine uEVR (standard deviation of the sample). Calculate UBI (bias). If the relation between the single influence factor of the systematic measurement error is known, the measuring system can be corrected using the bias.

Repeatability (on 2 standards near upper and lower specification limit) max uEVR

Measurement on 2 reference standards Determine the specification limits and adjust measuring points : zero and amplification. 2x15 repeated measurements are generally performed. Similar to measurement on one standard but at the upper and lower specification limit. For further investigation, it is recommended to use the highest standard uncertainty of uEVR1 and uEVR2.

Type A/B

A

A

Reference

Experiment Type 1 study [25]

Experiment 2x Type 1 study [25]

B Model If the influences of the adjusting procedure are known, a specific model can be created. In case of mechanical measuring equipment for length measurements, these are influence factors such as: form deviations, geometrical deviations of the working standards, positioning accuracy of the test part, manufacturing and assembling tolerances depending on the measuring system, sampling strategy, algorithms for evaluation, calibration and setting position

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Source of uncertainty

Suggestions / notes

Uncertainty from lin- Case 1 earity uLIN Using manufacturer’s specification Where value a is specified by the manufacturer: uLIN = 1 3 ⋅ a Case 2 Measurement on 3 reference standards Always a minim of 10 repeated measurements on each of 3 reference standards. Minimum sample size of 30. Standards must be clamped, released, and always measured in the same place of measurement. Case 3 Measurement on three or more reference standards (regression function) In order to apply this method, the regression function must be considered in the calculations performed by the measurement software. The evaluation of uLIN based on this method only provides the corrected values that are not taken into account on the measuring system. Reproducibility of Always 2 repeated measurements on each of operators (operator 10 test parts by 2 or 3 operators influence) using test Special case: If less than 10 test parts are parts uAV available, a minimum of 2 repeated measurements on a minimum of 3 test parts by 2-3 operators is required. Remark The test parts used in the experiment should be evenly spread over the entire tolerance zone. Test parts must be clamped, released, and always measured in the same place of measurement. Sequence for repeated measurements: Measure test parts 1 - n and repeat these measurements. In case of the series of measurements, the single operators must not remember the results of the previous measurement.

Type A/B

Reference

B

Manufacturer’s specification

A

Experiment with three standards see Annex E

A Experiment with standards see Chapter 5

A

Experiment Type 2 study [1], [25]

Determine uAV using the method of ANOVA.

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Source of uncertainty

Suggestions / remarks

Repeatability on test Always 2 repeated measurements on each of parts without opera- 25 test parts. tor influence uEVO Application in (semi-)automated measuring systems or whenever the operator does not affect the measurement result. Remark The test parts used in the experiment should be evenly spread over the entire tolerance zone. Test parts must be clamped, released, and always measured in the same place of measurement. Sequence for repeated measurements: Measure test parts 1 - n and repeat these measurements. In case of the series of measurements, the single operators must not remember the results of the previous measurement. The result includes the mutual interaction between test part, measuring system, etc. Reproducibility of the Relevant to min. 2 measuring systems equal measuring Evaluation systems (place of The following generally applies to standards: measurement) uGV Observe the variation per place of measurement Compare the measured quantity value x to the calibrated values (bias) Observe max – min of the measured quantity values x for the single equal measuring systems

Type A/B A

A

Reference Experiment Type 3 study [25]

Experiment Type 1 and Type 3 study [25]

The following generally applies to test parts: Observe the variation per place of measurement Observe max – min of the measured quantity values x or the measured individuals xi per test part for each equal measuring system. The result includes the mutual interaction between test part, measuring system, etc. The experimentally determined uncertainty components are considered by using the analysis of variance (ANOVA). Remark Make this evaluation by using the same working standards and test parts. Clamp, release and measure in the same place of measurement the test parts of the 2 n measuring systems.

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Source of uncertainty

Suggestions / remarks

Reproducibility over Short-term analysis time uSTAB In general, a short-term analysis does not inspect the stability of the measuring device. Long-term analysis If measurement results are assumed to change over time in an initial or basic sampling, the uncertainty should be determined by means of specified series of measurements. Ongoing review of the measurement process capability (stability) For an ongoing review of critical characteristics or measurement processes. Remarks Working standards or test parts can be inspected. The values are plotted, for example, on a control chart and the monitoring of measurement process is based on action limits. In case of an action limit violation, UMP must be corrected. Form deviation / sur- There are different methods in order to deterface texture / materi- mine the standard uncertainty from form deviaal property of the test tion: part uOBJ (uncertainty information from drawings (maximum permisfrom test part inho- sible form deviation) mogeneity) control chart of series production (actual form deviation) test part inspected in experiment (actual form deviation) The test parts (min. 5) used in the experiment shall be evenly spread over the entire tolerance zone and represent the expected form deviation. Any further properties, supposed or substantial, must be estimated separately by experiments or from tables and manufacturer’s specifications. Uncertainty from In order to determine the uncertainty from temtemperature perature, consider whether a compensation for uT temperature difference is made. Independent of compensation or complex relations including unknown expansion coefficients, the actual expansion properties should be determined experimentally. Heat the reference standards and test parts and inspect them while they are cooling. The difference a between max and min value is used in order to estimate uT.

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Type A/B A

Reference Experiments Type 1 study and Type 2 or Type 3 study [25]

(see Chapter 6.2)

B B

Drawing control chart

A

Experiment

Table book material data sheet

A/B

Experiment See Annex B

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Uncertainty from Any further influences, supposed or substanother influence com- tial, must be estimated separately by experiponents uREST ments or from tables and manufacturer’s specifications.

Table 14:

7.1

A/B

Experiment various documents

Methods recommended in order to determine uncertainty components

Overview of Typical Measurement Process Models

Many measurement processes are only affected by some or very few uncertainty components. For this reason, measurement process models can be defined based on equal uncertainty components (see Table 15). This overview provides help with the following questions: • What was the calibration uncertainty used in order to determine the actual value of the reference standard? • Can the purchased measuring equipment be accepted / approved for use? • What are the uncertainty components to be considered with standard measuring systems? • Are the measuring system (measuring instrument) and measuring equipment qualified for the respective specification(s)? How much do the production parts affect the measurement result or the capability of the measurement process? • What is the maximum variation of the measured quantity value? • Which factors must be considered in proving conformance or nonconformance (measurement result within or beyond the specification)?

Remark:

Models C, D and E (see Table 15) can be applied separately or are based on one another, i.e. the estimated uncertainties of model C can be transferred to model D or model E. They do not need to be determined once again.

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12 Other influences u Rest

11 Temperature uT

attribute measurement objets uOBJ

uStab 10 Form deviation/ surfaces - material

systems (measuring points) uGV 9 Reproducibility at different points in time

with serial parts uEVO 8 Reproducibility of equal measurement

partsn uAV 7 Repeatability without operator influence

6 Reproducibility of the operaor with serial

5 Linearity with master(s) uLIN

4 Repeatability with master(s) uEVR

3 Setting uncertainty uBI or Bias

MPE

2 Calibration uncertainty uCAL or error limits

1 Display resolution uRE Model A Calibration uncertainty of the reference Model B Acceptance study of the measurement process for standard measurement systems Model C Acceptance study of measurement systems Model D1 Acceptance study of the measurement process with user influence without serial part influence (measure serial parts location oriented) Model D2 Acceptance study of the measurement process without user influence without serial part influence (serial parts fed semi / automatically) Model E1 Conformity / acceptance study of the measurement process with user influence with serial part influence Model E2 Conformity / acceptance study of the measurement process without user influence with serial part influence (serial parts fed semi / automatically)

Measurement system Measurement process

green = always considered yellow = considered, if available gray = not considered for this model Table 15:

80

Typical measurement process models and their uncertainty components

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8

Special Measurement Processes

8.1

Measurement Process with Small Tolerances

Small tolerance is not a standardized term but it expresses that the tolerance is very small compared to normal conditions. Characteristic of small tolerances is that they are very hard to create and to measure. For this reason, the usual capability indices and ratios cannot be reached in the same way as those of normal tolerances. They often require conditions that are at the limits of what is physically and technically possible. Small geometric elements A small geometric element refers to very small measurement geometries available in a measurement. Only few data points can be recorded for a safe evaluation. Examples are measurements of very short lengths, measurements of very small radiuses or angular measurements where the legs of the angles are very short. In addition, the point of origin and the end point of the respective geometric element are often not clearly defined. This makes the situation even more difficult. Due to an uneven surface texture, the element does not have an ideal shape and thus, a higher measurement error must be expected. In individual cases, limits must be determined other than those mentioned in Chapter 4.8. It is not possible to determine a limit that generally applies to small tolerances because the limits also depend on the geometry and the physical and technical conditions in terms of the respective measurement task.

Remark:

8.2

Classification

In production processes including a high production variation, critical characteristics are often classified by dividing the tolerances of the relevant characteristics into two or more classes. Typical fields of application are: • • •

cylinder and piston cylinder and piston pin engine block and crankshaft

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The classification includes a 100% inspection of the relevant characteristics, the allocation of the parts to the respective class and a corresponding identification. The measurement uncertainty leads to different classifications, e.g. between manufacturer and customer, for results near the class limits obtained in repeated measurements. In order to ensure that the same parts can be assigned to a maximum of two adjacent classes in repeated measurements, the expanded measurement uncertainty is permitted to amount to a maximum of half the class width (KB): UMP / KB ≤ 0,5 In general: The maximum number of adjacent classes one part can be assigned to 2⋅UMP / KB +1 = maximum number of adjacent classes. class width KB

UMP UMP

UMP UMP

UMP UMP

tolerance

L lower specification limit

U upper specification limit

Figure 22: Classification model

8.3

Validation of Measurement Software

Current measuring instrument technologies use software applications in order to determine measured quantity values. The results provided by computer programs are not to be trusted blindly. Their diversity and complexity frequently make such computer programs error-prone. Even comprehensive tests conducted by the manufacturer cannot offer a guarantee that all “errors” have been found. Therefore, it is even more important to validate the

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software in order to prove that it meets the demands for the application in practice and that all relevant information is displayed completely. In order that software applications provide a very high level of correct results, several standards demand validation of the applied software: • Extract from DIN EN ISO 9001 [11] or ISO/TS 16949 [23] Chapter 7.6 “Control of monitoring and measuring equipment” By using computer software for monitoring and measuring specified requirements, the suitability of this software for the intended use must be confirmed. This confirmation must be provided prior to initial use and, where necessary, repeated later on. • Extract from ISO 10012 [12], Chapter 6.2.2 “Software”: Software used in the measurement processes and calculations of results shall be documented, identified and controlled to ensure suitability for continued use. Software, and any revision to it, shall be tested and/or validated prior to initial use, approved for use and archived. The typical range of the various computer programs used for monitoring and measuring specified requirements include measurement and evaluation programs for: •

coordinate measuring machines

•

measuring forms and surfaces

•

measuring systems / SPC systems

•

test benches

•

statistical evaluations

The demands on computer programs apply to third-party software and to the corporate software. A standardized procedure is recommended for an efficient validation. The validation shall be documented by means of an individual checklist. This list shall contain a reference to the following tasks, for example: • Compare release number on data storage medium to manual / information. • Document individual configuration and settings of the software. • Check important functions (to be specified for each respective application) after installation is completed.

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• Take measurements on calibrated reference standards and compare results to the calculated actual values and to the results of the previous version (also considering measurement uncertainty). • Check whether all relevant information is provided. • Compare results (e.g. obtained from multiple point measuring instrument) with more precise measuring system (e.g. coordinate measuring machine in measuring laboratory). • In order to make an evaluation, test data shall be provided with known results. This data is loaded, recalculted and the results are compared to the results of references. After completing the vaildation successfully: • approve the program explicitly for use. • Replace/update all installed systems concerned (if possible via network in order not to miss any individual system). • inform the users concerned about the latest software version. • sign a software maintenance contract, if possible, in order to be informed about any future upgrades (e.g. new guidelines, standards and legal regulations) automatically. Naturally, software is not subject to wear. For this reason, no further inspections of the validated software are required while it is used. However, the software must be validated again when changes in the system environment or to any signficant charcateristics of the software, hardware or the operating system take place. Ideally, the software producer / supplier provides a certificate of qualification (expert opinion).

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9

Capability Analysis of Attribute Measurement Processes

9.1

Introduction

Because of the nature of attributive measurements, it is only possible to obtain a satisfactory outcome regarding the capability of attribute measurement processes with a great deal of effort. A suitable approach for calculating the capability of attribute measurement processes must take into account that the probability of a particular test result is dependent on the type of characteristic. Hence, it is all about conditional probabilities. P (test result | value of the characteristic) The probability of a correct test result is nearly 100% for the values of the characteristic that lie beyond the areas of uncertainty around the specification limits. This probability is approximately 50% if the measurement results lie in the middle of the uncertainty range ("a decision by pure chance"). In principle, the proposed approach makes a distinction between the calculation of measurement capability without, or with reference values. In the case that reference values are available, a two-step approach is proposed.

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9.2

Capability Calculations without Using Reference Values

In this case, only a test of whether there are significant differences between operators can be made. But an assessment of whether the test has led to the correct result cannot be taken. However, this fact must always be considered when no reference values are present. The choice of test parts may have a decisive influence on the outcome of this test method, but it cannot be taken into account in this case. The following standard experiment is proposed: At least 40 different test parts should be tested 3 times by 2 different operators, called A and B. Each of the different measurement results on the 40 parts, which the operator A or operator B has achieved, is assigned to one of the following three classes. Class 1: Class 2: Class 3:

All three test results on the same part gave the result "good". The three test results on the same part gave different results. All three test results on the same part gave the result "bad".

The test results can be summarized in a table. Frequency nij

Operator A

Class 1 result “+++“ Class 2 different results Class 3 result “- - -“

Class 1 result “+++“

Operator B Class 2 different results

Class 3 result “- - -“

7

3

1

10

4

7

2

1

5

This table is now tested using a Bowker-Test of symmetry. If there are no significant differences between operators, the resulting frequencies nij in the above table will be sufficiently symmetrical with respect to the main diagonal.

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The hypothesis H0: mij = mji (i, j = 1, …, 3 where i ≠ j) says that the expected frequencies mij which lie symmetrical with respect to the main diagonal are identical. The test value

χ2 = ∑ i> j

(nij - n ji )2 nij + n ji

= 8,603

is compared to the test statistic with 3 degrees of freedom. The hypothesis on symmetry is rejected on the level if the test value is greater than the quantile in the χ² distribution with 3 degrees of freedom. Bowker-Test of symmetry of the expected frequencies Null hypothesis H0:

mij = mji (i, j = 1, …, 3 where i ≠ j) both operators obtain similar results

Alternate hypothesis H1:

mij ≠ mji both operators obtain different results

Test value:

χ2 = ∑ i> j

Test statistic:

Test decision:

(nij - n ji )2 nij + n ji

= 8,603

1-α fractile χ²1-α ; 3 quantile ---------------------------------------------0,90 6,251 0,95 7,815 0,99 11,345 0,999 16,266 The null hypothesis H0 is rejected with an error probability of α ≤ 5% because the calculated test value is greater than the test statistic, which is the 95 % fractile of the distribution.

Conclusion: The results of the two operators can be regarded as different.

In principle, this method is also to be used with more than 2 operators. In such cases, all operators take 3 repeatability tests on the test part and subsequently, all combinations of two combinations of operators should be tested individually. One should note that in this case the significance level is changed for the overall statements by these multiple tests.

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9.3

Capability Calculations Using Reference Values

9.3.1

Calculation of the Uncertainty Range

The signal detection approach requires test parts with known reference values. The purpose of the method is to determine the uncertainty range, in which an operator is unable to make an unambiguous decision. The following numeric example is taken from the MSA manual [1] where two further methods are explained that are not examined in this document.

for the last time corresponding „Rejection“ d = 0,566152 − 0,542704 U

= 0,023448

for the first time corresponding „Acceptance“

for the last time corresponding „Acceptance“ d = 0,470832 − 0,446697 U

= 0,024135 for the first time corresponding „Rejection“

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Symbols In the table, the reference measurement values are introduced in the form of a code. A plus sign means that all three operators have indicated the result from the test part as approved in all three tests, and that this assessment is consistent with the reference value. A minus sign means that all three operators have indicated the result from the test part as not approved in all three tests and that this assessment is consistent with the reference value. The symbol “X” indicates a case where at least one of the operators has come to a test result, which is not consistent with the reference value.

Working steps for determining the uncertainty range: Step 1: Sort the table according to the measured reference size. In the above example, a sorting in descending order is made - from the highest reference value descending to the lowest reference value. Step 2: Select the last reference value for which all operators have assessed all the results as being unsatisfactory (symbol “-“). This is the transition from symbol "–" to symbol "X". 0,566152 0,561457

X

Step 3: Select the first reference value for which all operators the first time assessed all results being approved (symbol “+”). This is the transition from symbol "X" to the symbol "+". 0,543077 0,542704

X +

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Step 4: Select the last reference value for which all operators last time assessed all the results as being approved (symbol ”+“). This is the transition from the "+" symbol to the symbol "X". 0,470832 0,465454

+ X

Step 5: Select the first reference value for which every operator has again first assessed all the results as unsatisfactory (symbol “-“). This is the transition from symbol "X" to the symbol "–". 0,449696 0,446697

X -

Step 6: Calculate the dU interval from the last reference value, for which all operators have assessed the result as unsatisfied to the first reference value, for which all operators have the result as approved. dU = 0,566152 – 0,542704 = 0,023448 Step 7: Calculate the dL interval from the last reference value, for which all operators have assessed the result as approved to the first reference value, for which all operators have the result as unsatisfied. . dL = 0,470832 –0,446697 = 0,024135 Step 8: Calculate the average d of the two intervals. d = (dU + dL) / 2 = (0,023448 + 0,024135) / 2 = 0,0237915

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Step 9: Calculate the uncertainty range. UATTR = d / 2 = 0,0237915 / 2 QATTR = 2 · UATTR / TOL = 2 ·( 0,0237915 / 2) / 0,1 ≈ 0,24 Then QATTR amounts to about 24 %.

Figure 23: Value chart plotting all reference values and the calculated uncertainty range Remark:

The effort for this method is considerable, as in this example in addition to the 50 reference measurements also at least 450 other test measurements have to be made and documented.

For the selection of test parts, it must be presumed that the uncertainty range will be covered. A maximum of the half tolerance must be covered around the specification limits. This region can be limited due to available information and by considering the resolution. A measurement process capability analysis requires that the limits of the real uncertainty range are determined.

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9.3.2

Ongoing Review

For ongoing review of the measurement process, at least one operator should measure at least 3 test parts all with defined reference values. The test parts should be selected in a way that the reference values are located within the zone I, II or III so that a clear result can be expected (all tests are consistent with the reference value).

UMP

UMP

UMP

UMP

The size of the uncertainty range can either be determined experimentally (see previous chapter), or derived from the actual defined requirements for an appropriate measurement process (QMP).

QMP =

2 UMP ⋅ 100% ≤ QMP _ max TOL

This leads to

UMP _ max =

QMP _ max ⋅ TOL 2 ⋅ 100%

It is to be taken into account that the extended uncertainty is usually given to be the 95,45 % level.

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10

Appendix

Annex A

Statistical Background of the Measurement Process Capability Analysis

Annex A.1

Formulas for Calculating the Regression Function

y nk = β0 + β1 ⋅ xn + ε nk Formulas for estimating the unknown parameters β0 (“y-intercept“) and β1 (”slope“): N

βˆ 1 =

∑ (x

n

- x ) ⋅ ( yn - y )

n -1

N

∑ (x

n

- x )²

n -1

βˆ0 = y − βˆ1 ⋅ x and the residuals enk: N

σˆ ² = where ynk

K

N

K

∑∑ (enk ) ² ∑∑ (ynk - yˆn ) ² n -1 n -1

N ⋅K - 2 th

=

n -1 n -1

where

N ⋅K - 2

yˆ n = βˆ0 + βˆ1 ⋅ x n

th

k of K measurements on the n of N standards th

xn

conventional true value for the n standard

εnk

N(0,σ ) distributed deviations of ynk from the expected value th (β0+β1·xn) obtained in the measurement on the n standard

2

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Annex A.2

ANOVA Tables

Since the uncertainty components affect the measurement results in the form of random errors (see Chapter 4.1), only ANOVA analyses of model II (random components of uncertainty only) are considered. Analysis of variance table referring to Chapter 5.2.2.2 LIN = linearity EVR = repeatability on standards

N = number of standards K = number of repetitions yn • = ∑ ynk

Mean of the values measured on standard n

yn • =

k

ynk K

Sum of squares

Degrees of freedom

LIN

SSLIN = ∑∑ ( ynk - yˆn ) ² - SSEVR

fLIN = N - 2

MSLIN =

SSLIN fLIN

EVR

SSEVR = ∑∑ ( ynk - yn • ) ²

fEVR = NK - N

MSEVR =

SSEVR fEVR

n

k

n

k

Mean square

Estimated variance

Estimated standard deviation

Test statistic F (F-Test)

Critical value F0

LIN

σˆ ² LIN = MS LIN

σˆ LIN = σˆ ²LIN

MSLIN MSEVR

F (1 - α , f LIN , f EVR )

EVR

σˆ ² EVR = MS EVR

σˆ EVR = σˆ²EVR

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Analysis of variance tables referring to Chapter 5.3.1 AV = operator’s reproducibility

NA = number of operators

PV = reproducibility part to part

NP = number of parts

IA = interaction operator - part

NR = number of repetitions

EVO = repeatability on parts

Case 1: Uncertainty components from repeatability

yp • =

Mean of the values measured on part p

y

••

SSPV = NR ∑ ( yp • − y

••

)²

SSEVO = ∑∑ ( ypr - yp •) ² p

r

Estimated variance

EVO

σˆ ²PV =

∑∑y

pr

r

Degrees of freedom

p

PV

=

p

Sum of squares

EVO

yp • NR y •• y •• = NRNP yp• =

pr

r

Overall mean

PV

∑y

fPV = NP − 1 fEVO = N P (NR - 1)

Estimated standard deviation

SSPV fPV SSEVO MSEVO = fEVO MSPV =

Test statistic F (F-Test)

Critical value F0

MSPV MSEVO

F (1- α , f PV , f EVO )

MSPV − MSEVO NR

σˆ ²EVO = MSEVO

Mean square

σˆ EVO = σˆ ²EVO

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Case 2:

Uncertainty components from operator, repeatability and interactions between operator and part

Mean of the values measured on part p by operator a

yap• = ∑ yapr

Mean of the values measured by operator a

ya • • = ∑∑ yapr r

y

Mean of the values measured on part p

∑∑y

=

• p•

p

a

a

p

Sum of squares

SSAV = NRNP ∑ ( ya • • − y

PV

SSPV = NRNA ∑ ( y

IA

SSIA = NR ∑∑ ( yap • − ya • • − y

• p•

fAV = NA − 1

MS AV =

SS AV fAV

•••

)²

fPV = NP − 1

MS PV =

SS PV fPV

f IA = (N A − 1)(N P − 1)

MSIA =

SSIA fIA

f EVO = N A N P (N R - 1)

MSEVO =

a

•p•

+y

p

SSEVO = ∑∑∑ (yapr − yap ) ² a

p

r

AV

σˆ ² AV =

MSAV − MSIA NPNR

PV

σˆ ²PV =

MSPV − MSIA NANR

IA

σˆ ²IA =

96

−y

MSIA − MSEVO NR

σˆ ² E VO = MS E VO

Mean square

)²

•••

a

Estimated variance

EVO

r

Degrees of freedom

AV

EVO

apr

r

y • •• = ∑∑∑ yapr

Overall mean

a

yap • NR ya • • ya • • = NRNP y •p• y • p• = NRNA y ••• y • •• = NR NA N P yap • =

r

•••

)²

SS EVO fEVO

Estimated standard deviation

Test statistic F (F-Test)

Critical value F0

σˆ AV = σˆ ² AV

MS AV MS IA

F (1 - α , f AV , f IA )

MS PV MS IA

F (1 - α , f P V , f IA )

MSIA MSEVO

F (1- α , f IA, fAVO )

σˆ IA = σˆ ²IA σˆ EVO = σˆ ²EVO

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If the interaction between the operator and the part is not significant, i.e. if F < F0, repeatability and interaction should be combined to a single component (pooling). Then:

MSPool =

SSPool fEVO + fIA

•

SSPool = SSEVO + SSIA

•

MSPool replaces MSIA in the AV and PV line of the variance table.

•

The estimated standard deviation from repeatability is

and

σˆ EVO = MSPool Case 3: Uncertainty components from measuring system, repeatability and interaction between measuring system and part Similar to case 2, but replacing the operator by the measuring system.

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Annex B

Estimation of Standard Uncertainties from Temperature

Since most materials change as the temperature varies, the standard uncertainty from temperature uT must be determined in all measurements (Figure 24).

Figure 24: Determining the standard uncertainty from temperature uT

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In comparing a test part (work part) to a reference standard or a scale, temperature variations do not affect the measurement result if the test part and the reference standard or scale are made of the same material and have the same temperature. If this is not the case, the measurement result is subject to an uncertainty caused by different expansion coefficients. Since these temperature variations can be quite high, the results should generally be corrected for these variations mathematically (compensation for temperature difference). Annex B.1 Uncertainty with Correction of Different Linear Expansions The calculation of corrected measured quantity value ycorr depends on the type of measurement: Absolute measurement

y corr = where

yi ∆TOBJ ∆TR

αOBJ αR

y i ⋅ (1 + α R ⋅ ∆TR ) 1 + αOBJ ⋅ ∆TOBJ

B.1

= value displayed by the measuring instrument = test part’s deviation of temperature from 20° C = reference standard’s deviation of temperature from 20° C = thermal expansion coefficient of test part = thermal expansion coefficient of reference standard (e.g. glass scale of a height gauge)

If a good approximation is available, the following formula applies:

y corr ≈ y i ⋅ 1 − (α OBJ ⋅ ∆TOBJ − α R ⋅ ∆TR )

B.2

Comparison measurement

y corr =

y R ⋅ (1 + α R ⋅ ∆TR ) + d 1 + αOBJ ⋅ ∆TOBJ

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where : d yR

= =

∆TR

=

αR

=

temperature difference (test part – reference standard) length of reference standard at reference temperature of 20° C reference standard’s deviation of temperature from 20° C thermal expansion coefficient of reference

If a good approximation is available, the following formula applies:

y corr ≈ y R + d + y R (α R ⋅ ∆TR − αOBJ ⋅ ∆TOBJ )

B.4

Since the (measured) temperatures and the thermal expansion coefficients used in the calculation also cause an uncertainty, an uncertainty from other influence components uREST remains. Assuming that αOBJ , αR , ∆TOBJ and ∆TR are uncorrelated and that there are no changes in temperature during the measurement, the standard uncertainty from temperature is calculated by: 2 2 uT = uREST = y i ; y R ∆TR2uα2R + ∆TOBJ uα2OBJ + α R2 u∆2TR + αOBJ u∆2TOBJ

B.5

In case no further data is available, the uncertainty from expansion coefficients is assumed to be 10 % of these coefficients and the uncertainty from temperature amounts to 1 Kelvin. If temperature variations (drifts) might occur during the measurement, these influences must possibly also be considered. As an example, Table B.1 lists uncertainties from other influence components caused in measurements on test parts made of different materials and by using different scales or reference standards. All these examples are based on the assumption that the temperature of the test part and the measuring instrument is nearly the same (test part has been controlled) and that the temperature is constant during the measurement. It is also assumed that uα = 1 Kelvin . = 0,1⋅ αOBJ;R and u∆T OBJ ;R

100

OBJ ;R

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System without

expansion α R ≈ 0 1/K

Glass

Ceramic

Steel

Gauge Standard

Material of the test part

Aluminium -6 αOBJ = 24 10 1/K Brass -6 αOBJ = 18 10 1/K Steel -6 αOBJ = 11,5 10 1/K Cast iron -6 αOBJ = 10 10 1/K Aluminium -6 αOBJ = 24 10 1/K Brass -6 αOBJ = 18 10 1/K Steel -6 αOBJ = 11,5 10 1/K Cast iron -6 αOBJ = 10 10 1/K Aluminium -6 αOBJ = 24 10 1/K Brass -6 αOBJ = 18 10 1/K Steel -6 αOBJ = 11,5 10 1/K Cast iron -6 αOBJ = 10 10 1/K Aluminium -6 αOBJ = 24 10 1/K Brass -6 αOBJ = 18 10 1/K Steel -6 αOBJ = 11,5 10 1/K Cast iron -6 αOBJ = 10 10 1/K

Table B.1:

Uncertainty from other influence components uT in µm per 100 mm with a temperature deviation ∆TOBJ;R from 20° C 0K

2,5 K

5K

7,5 K

10 K 12,5 K 15 K

2,7

2,7

3,0

3,3

3,8

4,3

4,8

2,1

2,2

2,4

2,7

3,0

3,4

3,9

1,6

1,7

1,8

2,0

2,3

2,6

2,9

1,5

1,6

1,7

1,9

2,2

2,4

2,7

2,6

2,7

2,9

3,2

3,7

4,1

4,7

2,0

2,1

2,3

2,5

2,9

3,3

3,7

1,5

1,5

1,7

1,9

2,1

2,4

2,7

1,4

1,4

1,5

1,7

2,0

2,2

2,5

2,5

2,6

2,8

3,2

3,6

4,0

4,6

2,0

2,0

2,2

2,5

2,8

3,2

3,6

1,4

1,4

1,6

1,8

2,0

2,2

2,5

1,3

1,3

1,4

1,6

1,8

2,1

2,3

2,4

2,5

2,7

3,0

3,4

3,8

4,3

1,8

1,9

2,0

2,3

2,5

2,9

3,2

1,2

1,2

1,3

1,4

1,6

1,8

2,1

1,0

1,0

1,1

1,3

1,4

1,6

1,8

Standard uncertainty uT from test parts made of different materials using different scales or reference standards in case a compensation for temperature difference is made (in this table K stands for Kelvin)

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Annex B.2

Uncertainty without Correction of Different Linear Expansions

Since most cases occurring in practice do not allow for a correction by calculation, errors that are caused by different expansions at temperatures deviating from 20° C must also be considered. The following procedure is based on the assumption that the temperature of the test part and the measuring instrument is nearly the same during the measurement (test part has been controlled) and that a specified maximum temperature deviating from 20° C is not exceeded. The greatest possible measurement error that can occur at a maximum temperature tmax is regarded as the error limit a caused by temperature influences. Note 1:

This approach particularly applies to temperature-controlled measuring laboratories where the actual temperature is stable between a reasonable maximum and a minimum temperature around the reference temperature of 20° C.

Note 2:

If a high maximum temperature is permissible, its resulting uncertainty component frequently makes up a major part of the uncertainty budget and often causes an unsatisfactory expanded measurement uncertainty UMP that is extremely high.

Due to different linear expansions at the maximum temperature tmax, the measurement error ∆yi, in case of a good approximation, is calculated by:

∆y ≈ y i ; y R ⋅ (t max − 20 ° ) ⋅ (α OBJ − α R )

B.6

This measurement error is added to the uncertainty from different expansion coefficients αR or αOBJ (at tmax) and leads to the maximum permissible error a (worst case) caused by temperature variations.

a = ∆y i + 2u REST

where

2 uREST = y i ; y R ⋅ ∆TR2 ⋅ uα2R + ∆TOBJ ⋅ uα2OBJ

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B.7

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Thereby uREST is calculated as described in formula B.5, but leaving out the uncertainty components of the temperature measurement that was not tak2 en in this case ( α R2 ⋅ u∆2T = 0 and αOBJ ⋅ u∆2T = 0 ). R

OBJ

This leads to the standard uncertainty from temperature:

uT =

a

B.8

3

As an example, Table B.2 lists uncertainties from other influence components caused in measurements on test parts made of different materials using different scales or reference standards when the different linear expansions where not corrected by calculation. It is assumed that uα = 0,1⋅ αOBJ;R . OBJ ;R

Note 1:

Strictly speaking, the uncertainty calculated by the methods described above only applies to rod-shaped test parts with a homogenous temperature. By contrast, it is difficult to estimate the thermal expansion and thus the uncertainty from expansion coefficients for any other, particularly asymmetric test parts. However, the uncertainty generally only becomes smaller compared to the rod-shaped test part so that one is always “on the safe side”.

Note 2:

The tables show that a different thermal expansion coefficient of the test part and the reference standard result in high uncertainties. This leads to the conclusion that measuring instruments including scales with very small thermal expansion coefficients cause a high measurement uncertainty if a compensation for temperature difference is not made. In general, these measuring instruments require a correction of temperature influences by calculation.

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Gauge

Standard

Steel Ceramic

Aluminium -6 αOBJ = 24 ⋅ 10 1/K Brass -6 αOBJ = 18 ⋅ 10 1/K Steel -6 αOBJ = 11,5 ⋅ 10 1/K Cast iron -6 αOBJ = 10 ⋅ 10 1/K Aluminium -6 αOBJ = 24 ⋅ 10 1/K Brass -6 αOBJ = 18 ⋅ 10 1/K Steel -6 αOBJ = 11,5 ⋅ 10 1/K Cast iron -6 αOBJ = 10 ⋅ 10 1/K Aluminium -6 αOBJ = 24 ⋅ 10 1/K Brass -6 αOBJ = 18 ⋅ 10 1/K Steel -6 αOBJ = 11,5 ⋅ 10 1/K Cast iron -6 αOBJ = 10 ⋅ 10 1/K Aluminium -6 αOBJ = 24 ⋅ 10 1/K Brass αOBJ = 18 ⋅ 10-6 1/K Steel -6 αOBJ = 11,5 ⋅ 10 1/K Cast iron -6 αOBJ = 10 ⋅ 10 1/K

System without expansion α R ≈ 0 1/K

Glass

Material of the test part

Table B.2:

104

Uncertainty from other influence components uT in µm per 100 mm with a temperature deviation ∆TOBJ;R from 20° C 0,5 K

1K

2,5 K

5K

7,5 K

10 K

15 K

0,5

1,0

2,6

5,1

7,7

10,3

15,4

0,3

0,6

1,6

3,1

4,7

6,2

9,3

0,1

0,2

0,5

0,9

1,4

1,9

2,8

0,1

0,3

0,7

1,3

2,0

2,6

3,9

0,6

1,1

2,8

5,7

8,5

11,4

17,0

0,4

0,7

1,8

3,6

5,4

7,3

10,9

0,1

0,3

0,7

1,4

2,2

2,9

4,3

0,1

0,2

0,5

0,9

1,4

1,9

2,8

0,6

1,2

3,0

6,1

9,1

12,2

18,2

0,4

0,8

2,0

4,0

6,0

8,0

12,1

0,2

0,4

0,9

1,8

2,7

3,6

5,5

0,1

0,3

0,7

1,3

2,0

2,6

4,0

0,8

1,7

4,2

8,3

12,5

16,6

24,9

0,6

1,2

3,1

6,2

9,4

12,5

18,7

0,4

0,8

2,0

4,0

6,0

8,0

12,0

0,3

0,7

1,7

3,5

5,2

6,9

10,4

Standard uncertainty uT from test parts made of different materials using different scales or reference standards in case a compensation for temperature difference is not made (in this table K stands for Kelvin)

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Annex C

Reducing the Measurement Uncertainty by Repeating and Averaging Measurements

The measurement uncertainty can be reduced by repeating and averaging measurements. By taking repeated measurements instead of an individual measurement, the random measurement uncertainty components can be reduced by a factor of n * . Prior to that, the standard uncertainty must be determined based on 25 repeated measurements under equal conditions of measurement, i.e. the standard deviation of a previous series of measurements is used in order to express the measurement uncertainty (cf. Chapter 5). The figure below shows how raising the number of measured quantity values n* reduces the standard uncertainty. 100

extension of measurement uncertainty

80

60

40

20

0 1

5

9

13

17

21

25

No. of measurements

Figure A.D.1: Reducing the measurement uncertainty by raising the number of repeated measurements n*

In case of an individual measurement of a characteristic, the experimentally determined repeatability of the measuring instrument is included in the uncertainty budget in the form of uEVR or uEVO (cf. Chapter 5.2 and 5.3). If a measurement result is obtained by repeating and averaging the measurement of one characteristic, the influence of the variation is reduced. The uncertainty component from repeatability on test parts is not calculated from the variation of individual measured quantity values but from the smaller random variation of the means of these measured values.

uEVO * =

uEVO n*

.

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where n* is the number of measurements required for averaging the measurement. In the uncertainty budget, the uncertainty uEVO* replaces the uncertainty uEVO that was determined experimentally during the capability analysis. It is important to consider that only the greatest value of uEVR, uEVO or uRE is considered in the uncertainty budget. For this reason, the standard uncertainty from repeatability on standards uEVR must always be replaced by uEVR* which is reduced by a factor of n * . It must also be compared to the uncertainty from resolution of the measuring system uRE. Example: An experiment led to the following uncertainty budget: uCAL = 0,8 µm, uEVR = 0,9 µm, uEVO = 1,1 µm, uRE = 0,6µm, uAV = 1,3 µm measured quantity value of individual measurement: ø 20,354 mm The combined standard uncertainty

{

}

2 2 2 2 2 uMP = uCAL + max u EVR ;uEVO ;uRE + u AV

is calculated using the uncertainty components listed above: 2 2 2 uMP = uCAL + uEVO + u AV = 0,8 2 + 1,12 + 1,3 2 = 1,88 µm.

measurement result: ø 20,354 mm ± 3,76 µm (k=2). measured quantity values of repeated measurement: ø 20,354 mm; ø 20,348 mm; ø 20,352 mm Based on n* = 3 repeated measurements, the uncertainty amounts to uEVO* = 1,1 3 = 0,64 µm or uEVR* = 0,9 3 = 0,52 µm, whereby uMP is reduced 2 2 2 uMP = uCAL + uEVO 0,8 2 + 0,642 + 1,3 2 = 1,66 µm. * + u AV = measurement result: ø 20,3513 mm ± 3,32 µm (k=2). If the number of repeated measurements is raised once again, e.g. to n* = 5, the uncertainty is even more reduced uEVO* = 1,1 5 = 0,49 µm or uEVR* = 0,9 5 = 0,40 µm. However, this does not lead to a considerable improvement of the measurement result because the uncertainty from resolution uRE = 0,6 is the greatest uncertainty component. Thus, it is the only component of the measuring instrument to be considered in the result. 2 2 2 uMP = uCAL + uRE + uAV = 0,82 + 0,62 + 1,32 = 1,64 µ m .

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Annex D

k Factors

If the specified design of experiments cannot be realized in terms of the demanded sample size, it is necessary to take a Student t-distribution instead of the standard normal distribution to estimate the uncertainty components. This will then result in the expanded measurement uncertainty: U MP = t f ,1-α / 2 ⋅ u MP The number of degrees of freedom f is obtained from the product of the number of test parts, the number of operators, the number of measuring systems and the number of repeatability measurements reduced by one. For

f = 3 ⋅ 2 ⋅ 2 ⋅ (3 − 1) = 24 one will find

t 24,1-α / 2 = 2,11 ,

For

f = 3 ⋅ 2 ⋅ 2 ⋅ (2 − 1) = 12 one will find

t12,1-α / 2 = 2,23 .

degree of freedom f k values (p=95,45%)

Table 15:

1

2

3

4

5

6

7

8

9

10

11

12

13

14 → ∞

13,97 4,53 3,31 2,87 2,65 2,52 2,43 2,37 2,32 2,28 2,25 2,23 2,21 2,20 2,0

k values for a 95,45% level of confidence according to the respective degree of freedom

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Annex E

Setting Working Point(s)

Before a measuring system can be applied for measurements, it must normally be set using one or two reference standard(s). The measuring system is set according to the calibrated actual value of the standard (working standard) which makes the system ready for use. Depending on the measurement procedure or measuring system, there are different methods available in order to set the system. Setting a working point using a calibrated reference standard Determination of the systematic measurement error and the repeatability (Type 1 study): display

real

ideal y = x

b

0

measured value

This method is applied to linear measuring systems for setting the working point. The value of the reference standard shall lie within an area of +/-10 % around the working point.

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Setting working points using two calibrated reference standards Determination of the systematic measurement error and the repeatability (Type 1 study):

Case 1 display

This method is applied to linear measuring systems for setting zero on the system or for boosting. The values of the reference standard shall lie within an area of +/-10% around the zero point and the upper working point. The uncertainty components are determined from the repeatability variation on the reference standards and from the deviations of the calculated means from the calibrated actual values of the reference standards (using the greatest value in each case). Case 2

real

ideal y = x 2. reinforcement set gradient

1. set zero point 0

measured value

display

This method is used in order to set the upper and lower specification limit on the measuring system. The values of the reference standard shall lie within an area of +/-10% around the limits. The uncertainty components are determined from the repeatability variation on the reference standards and from the deviations of the calculated means from the calibrated actual values of the reference standards (using the greatest value in each case).

real

ideal y = x

0

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L

U

measured value

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Annex F

Calculation Examples

Annex F.1

Measurement Process Capability Using 3 Standards

An instrument measuring boltholes requires that the capability of the measurement process for inside diameters should be established and documented. Uncertainties from test part or the temperature are regarded as negligible and are not considered in the evaluation. Information about measuring system and measurement process Nominal dimension

30,000 mm

Upper specification limit U

30,008 mm

Lower specification limit L

30,003 mm

Resolution of the measuring system RE (1 digit = 0,0001mm)

0,1 µm

Calibration uncertainty UCAL

0,026 µm

Coverage factor kCAL

2

Linearity

0

Reference quantity value of the standard at the upper specification limit xmu

30,0076 mm

Reference quantity value of the standard in the centre of the specification xmm

30,0050 mm

Reference quantity value of the standard at the lower specification limit xml

30,0025 mm

Capability ratio limit measuring system QMS_max

15%

Capability ratio limit measurement process QMP_max

30%

In order to determine the standard uncertainties from repeatability on standards and from measurement bias, an experiment was conducted performing 10 repeated measurements on each of 3 reference standards.

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The information about the measuring system and the measured quantity values gained in the experiment leads to the following uncertainty budget and overview of results.

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Due to a percentage resolution %RE of 2,00% and a capability ratio QMS of 7,86%, the capability of the measuring system of the instrument measuring boltholes is established. After the capability of the measuring system is established, the measurement process is analyzed. The operator influence, the repeatability on test parts and their interactions are determined experimentally under operational conditions. In this experiment, 2 repeated measurements are performed on each of 10 test parts by 3 operators.

Based on the recorded measured quantity values, the individual standard uncertainties can be determined and allocated by using the method of ANOVA. This leads to the following uncertainty budget and overview of results for the measurement process.

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Due to a capability ratio QMP of 14,98% in case of a process capability ratio limit QMP_max of 30%, the capability of the measurement process of the instrument measuring boltholes is established.

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Annex F.2

Process Capability Using a D-optimum Design

Analogous to the example in Annex F.1, a new measurement process capability analysis should be made for the instrument measuring boltholes. However, in this case, the additional uncertainty component caused by the test part influence shall be considered. It is determined by taking further measurements at 4 different measuring points of the inside diameter. In order to minimize the effort for this experiment, the experiment is reduced to a minimum of measurements with the help of a D-optimum experimental design. The specifications, measured quantity values and results of the measuring system are the same as in the example of Annex F.1 and can be transferred to this example. For the measurement process, a D-optimum experimental design is created including 2 repeated measurements at each of 4 measuring points of 10 test parts by 3 operators. The D-optimum experimental design reduces the effort involved from 240 to 128 individual measurements. These are taken in random combinations of operator/test part/measuring point and evaluated by using the method of ANOVA. The information about the measuring system (see Annex F.1) and the measured quantity values of the D-optimum experimental design lead to the following uncertainty budget and overview of results.

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Table 16:

Measured quantity values of the D-optimum experimental design

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Due to a capability ratio QMP of 20,38% in case of a process capability ratio limit QMP_max of 30%, the capability of the measurement process of the instrument measuring boltholes is established.

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Annex F.3

Measurement Process Capability of a CMM

Measuring the inside diameter of a pump housing on a reference standard by using a coordinate measuring machine requires that the capability of the measurement process is established and documented.

Information about measuring system and measurement process Nominal dimension

150,00 mm

Upper specification limit U

150,02 mm

Lower specification limit L

149,98 mm

Resolution of the measuring system RE (1 digit = 0,0001mm)

0,1 µm

Reference quantity value of the standard

150,0015 mm

Calibration uncertainty UCAL

2 µm

Coverage factor kCAL

2

Linearity

0

Capability ratio limit measuring system QMS_max

15%

Standard uncertainty from expansion coefficients of the test part uαOBJ

1 10 /K

Mean temperature of the measurement process

22° C

Value displayed by measuring system

150,00 mm

Capability ratio limit measurement process QMP_max

30%

-6

In order to determine the standard uncertainties from repeatability on standards and from measurement bias, 20 repeated measurements were performed on a reference standard. Since the linearity deviation is zero, the linearity can be neglected.

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The information about the measuring system and the measured quantity values gained in the experiment lead to the following uncertainty budget and overview of results.

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Due to a percentage resolution %RE of 0,25% and a capability ratio QMS of 14,42%, the capability of the measuring system of the CMM is established. Since the measurement process capability only refers to one reference standard and a CMM does not involve a classical operator influence, the uncertainty from temperature is considered for this measurement process as described in ISO/TS 15530-3 [16]. This leads to the following uncertainty budget and overview of results for the measurement process.

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Due to a capability ratio QMP of 14,73% in case of a process capability ratio limit QMP_max of 30%, the measurement process capability of the CMM for measuring the inside diameter on a reference standard is established.

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Annex F.4 Measurement Process Capability of Automated Test Device The measurement process capability of automated test device must be established and documented. Information about measuring system and measurement process Nominal dimension

53,01 mm

Upper specification limit U

53,03 mm

Lower specification limit L

52,99 mm

Resolution of the measuring system RE (1 digit = 0,0001mm)

0,5 µm

Calibration uncertainty UCAL

1,6 µm

Coverage factor kCAL

2

Linearity uLIN (from preliminary investigation)

0

fmax of dial gauge (MPE)

1,2 µm

Reference quantity value of standard

53,0105 mm

Capability ratio limit of measuring system QMS_max

15%

Expansion coefficient α of test part for steel

11,5 1/K ⋅ 10 /K

Expansion coefficient α of measuring system for steel

11,5 1/K ⋅ 10 /K

Standard uncertainty from expansion coefficients of test part uαOBJ for steel

1,2 1/K ⋅ 10 /K

Standard uncertainty from expansion coefficients of measuring system uαR for steel

1,2 1/K ⋅ 10 /K

Maximum temperature (environment)

25° C

Delta temperature of working standard at 20°C

5° C

Delta temperature of working standard at 20°C

10° C

Value displayed by measuring system

53 mm

Capability ratio limit measurement process QMP_max

30%

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-6

-6

-6

-6

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measured at system level

In order to determine the standard uncertainties from repeatability on standards and from measurement bias, 25 repeated measurements were performed on the reference standard. A preliminary investigation did not detect any linearity deviations, so linearity must not be considered.

The information about the measuring system and the measured quantity values gained in the experiment lead to the following uncertainty budget and overview of results.

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Due to a percentage resolution %RE of 1,25% and a capability ratio QMS of 11,54%, the measuring system capability of the automated measuring equipment is established.

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After observing the measuring system, the measurement process of the automated test device is analyzed. In an experiment, 2 repeated measurements are performed on each of 10 test parts.

In addition to the repeatability on test parts, the temperature influence must also be considered. It is calculated from the difference between the expansion of the working standard and the test part and from the general uncertainty from temperature without correcting the linear expansion. This leads to the following uncertainty budget and overview of results.

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Due to a capability ratio QMP of 21,67% in case of a process capability ratio limit QMP_max of 30%, the measurement process capability of the automated test device is established.

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Annex F.5 Measurement Process Capability of a Multiple-point Measuring Instrument The measurement process capability for a multiple-point measuring instrument with 3 equal measuring points must be established and documented. First, the measuring system is observed by considering the influence factors of resolution, calibration uncertainty on standards, repeatability on standards, bias and sensor/touching as additional uncertainty components. Information about measuring system Nominal dimension

64,505 mm

Upper specification limit U

64,530 mm

Lower specification limit L

64,480 mm

Resolution of the measuring system RE (1 digit = 0,0001mm)

0,1 µm

Calibration uncertainty UCAL

1,8 µm

Coverage factor kCAL

2

Linearity uLIN (from preliminary investigation)

0

Error limit of sensor / by touching

0,8 µm

Reference value standard 1/meas. point 1

64,5042 mm

Reference value standard 1/meas. point 2

64,5035 mm

Reference value standard 1/meas. point 3

64,5016 mm

Reference value standard 2/meas. point 1

64,5421 mm

Reference value standard 2/meas. point 2

64,5449 mm

Reference value standard 2/meas. point 3

64,5465 mm

Reference value standard 3/meas. point 1

64,4604 mm

Reference value standard 3/meas. point 2

64,4612 mm

Reference value standard 3/meas. point 3

64,4596 mm

Capability ratio limit of measuring system QMS_max

15%

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Information about measurement process -6

Expansion coefficient α of test part for steel

11,5 1/K 10 /K

Expansion coefficient α of measuring system for steel

11,5 1/K 10 /K

Standard uncertainty from expansion coefficients of test part uαOBJ for steel

1,2 1/K 10 /K

Standard uncertainty from expansion coefficients of measuring system uαR for steel

1,2 1/K 10 /K

Maximum temperature (environment)

30° C

Value displayed by measuring system

64,505 mm

error limit from compensation for temperature difference

2,2 μm

Capability ratio limit measurement process QMP_max

30%

-6

-6

-6

In order to determine the standard uncertainties from repeatability on standards and from measurement bias, 10 repeated measurements on each of 3 reference standards were performed in an experiment.

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The information about the measuring system and the measured quantity values gained in the experiment lead to the following uncertainty budget and overview of results.

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Due to a percentage resolution %RE of 0,2% and a capability ratio QMS of 12,69%, the measuring system capability of the multiple-point measuring instrument is established. Secondly, the entire measurement process is observed. In an experiment, the influence factors of repeatability on standards, reproducibility of places of measurement and of their interactions are determined. Moreover, the temperature influence after the calculation without correcting linear expansion and a residual uncertainty from compensation for temperature difference are considered. In order to calculate the residual uncertainty from compensation for temperature difference, an individual experiment was conducted during a preliminary investigation (measured quantity value plotted on the temperature sequence/cooling curve is constant) and a error limit of 2,2 μm was determined. In the experiment for the measurement process, 2 repeated measurements were taken at every measuring point on each of 10 test parts. The recorded measured quantity values are evaluated using the method of ANOVA.

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The information about the measuring system and the measured quantity values gained in the experiment lead to the following uncertainty budget and overview of results.

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Due to a capability ratio QMP of 21,03% in case of a process capability ratio limit QMP_max of 30%, the measurement process capability of the multiplepoint measuring instrument is established.

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Annex F.6

Optimizing a Measurement Process

During an in-process inspection, the diameter of an engine shaft shall be measured. For this purpose, a qualified measuring system must be selected in order to evaluate the entire measurement process. A first review is based on a measuring system composed of a precision snap gauge, a mechanical dial gauge and a working standard. A general selection and evaluation of the measuring system / measurement process is based on the general data about the respective measurement component (mechanical dial gauge, precision snap gauge, working standard, etc.) rather than on specific individual data.

Engine shaft specifications Nominal dimension

8 mm

Upper deviation

+0,010 mm

Lower deviation

+0,001 mm

Upper specification limit U

8,010 mm

Lower specification limit L

8,001 mm

Roundness

0,003 mm

Information about mechanical dial gauge Resolution of the measuring system RE (1 digit = 0,0005 mm)

0,5 µm

Deviation range ftotal (MPE)

0,6 µm

Measuring interval

+/- 25 µm

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Information about precision snap gauge Parallelism (according to specification)

0,6 µm

Measuring force

3-10 N

Adjustment range

0 – 30 mm

Measuring span

2 mm

Measuring surfaces

D 8 mm

Information about working standard Reference value of standard

8,0005 mm

Calibration uncertainty UCAL

0,6 µm

Coverage factor kCAL

2

Temperature during calibration

20° C

Linearity uLIN

0

Before a measuring system can be applied for measurements, it must be set using a standard. The measuring system is set according to the calibrated actual value of the standard (working standard) which makes the system ready for use. In order to check this procedure, 25 repeated measurements on the standard are performed and the uncertainty from “repeatability” and “measurement bias” is determined.

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Remark:

Even if a measuring system was set using a reference standard, the limits of error of the dial gauge and the deviations of the precision snap gauge must be considered. Although the repeatability and systematic measurement error are known for this working point, they are unknown for measured quantity values lying around this working point. For values around the working point, the manufacturer of the measuring system only guarantees measurement results that do not exceed the specified limits of error (MPE). The same applies to the parallelism of the measuring surfaces and the setting using the standard. In this case, the deviations for the setting point (actual value of the working standard) are known, but they do not apply to lower or higher measured quantity values automatically.

A previous inspection confirmed that the deviations caused by temperature variations are negligible when the system is set once an hour because the materials have similar thermal expansion coefficients. The specifications, information and measured quantity values lead to the following uncertainty budget and overview of results.

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The overview of results shows that the capability of the measuring system with the mechanical dial gauge is not established due to a low resolution and a capability ratio QMS of 26,62% that is too high. Corrective action is taken by replacing the mechanical dial gauge by an incremental gauge with a lower MPE.

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Information about incremental gauge Resolution of the measuring system RE (1 digit = 0,0001 mm)

0,1 μm

MPE of incremental gauge

0,1 μm

Measuring interval 12 mm

12000 µm

In this case, the measuring system must also be set using a standard at first. The measuring system is set according to the calibrated actual value of the standard (working standard) which makes the system ready for use. In order to check this procedure, 25 repeated measurements on the standard are performed and the uncertainty from “repeatability” and “measurement bias” is determined.

The specifications, information and measured quantity values lead to the following uncertainty budget and overview of results for the measuring system with incremental gauge. Since the resolution is already included as an uncertainty component in the repeated measurements, it is not considered twice.

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The inspection of the measuring system with an incremental gauge shows that the resolution is sufficiently high, however, the capability ratio QMS exceeds the capability ratio limit QMS_max. As the uncertainty budget shows, capability cannot be established because of the influence of the parallelism of the precision snap gauge and the calibration uncertainty on the working standard. The next corrective action to be taken is to test a non-contact measuring instrument (laser micrometer). In this case, the measurement result is not affected by the main mechanical influence factor (parallelism of the precision

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snap gauge and calibration uncertainty on the working standard). The laser micrometer is calibrated by the manufacturer over the measuring interval and is ready for use immediately after it is switched on. Compared to the previous measuring systems, a laser micrometer need not be set using a working standard for the specified MPE range. Information about laser micrometer Resolution of the measuring system RE (1 digit = 0,0001 mm)

0,1 μm

Linearity deviation

0,2 µm

MPE of laser micrometer (calibrated at 20° C)

0,4 μm

Ambient temperature during the analysis of measured quantity values

26,5° C

In order to establish the measuring system capability of the laser micrometer under real conditions, 25 repeated measurements at the same measuring point of the standard is performed.

The measured quantity values and resolution of the measuring system lead to the following uncertainty budget.

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The uncertainty budget shows a high uncertainty from the measurement bias. This high influence is caused by the fact that all the recorded measured quantity values deviate from the reference quantity value of the standard uniformly because the reference quantity value of the standard was calibrated at 20° C. However, the laser micrometer measured the standard at an ambient temperature of 26,5° C. Due to the temperature variation, the reference standard is subject to linear expansion according to the formula:

Δl

ΔT α l

Expansion coefficient of reference standard: α (steel) = 11,5 +/-1 in 10−6 K−1 at 20° C Δ l = 6,5 * 11,5*10-6 * 8,0005 * = 0,598 µm = 0,6 µm. If the reference quantity value of the standard is reduced by 0,6 μm, the following uncertainty budget and the associated evaluations are obtained.

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Since a MPE is specified for the laser micrometer, the MPE is used for establishing measuring system capability in order to reduce the effort for the experiment. This leads to the following uncertainty budget and the associated evaluation of the measuring system.

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The overview of results shows that the measuring system of the laser micrometer meets the demands on the resolution %RE and the capability ratio QMS. The capability of the measuring system is established. In the next step, the measurement process is observed. In an experiment, 3 operators take 2 repeated measurements on each of 10 engine shafts.

This leads to an expanded uncertainty budget for the measurement process.

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Due to a capability ratio QMP of 13,48% in case of a process capability ratio limit QMP_max of 30%, a first review of the measurement process (without long-term analysis) establishes capability. The process can be used in production. In order to prove conformance or non-conformance, the form deviation (roundness) must be considered as a further influence factor affecting the test part. The following example is based on the information from a drawing where the maximum permissible measurement error amounts to 0,003 mm. Remark:

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Since a roundness figure always refers to a radius, it must be multiplied by a factor of 2 in order to analyze a diameter.

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The conformity evaluation shows that the permissible roundness results in a capability ratio QMP exceeding the process capability ratio limit QMP_max considerably. Thus, the capability of the entire measurement process including the maximum permissible measurement error is not established anymore. Corrective action can be taken by using a measurement method performing several measurements on the diameter of the engine shaft to be measured, By using laser micrometer, it is possible to record the mean, maximum and minimum value of a measurement e.g. in one revolution or in several measurements on the diameter. This method helps to reduce the uncertainty from form deviations considerably because the maximum and minimum diameters are actually measured. Thus, the customer is guaranteed that both diameters stay within the limits in the context of measurement uncertainty.

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The uncertainty from the minimum and maximum diameter of the manual measurement method was determined experimentally and amounts to R = 0,6 µm. Since the diameter was only measured at one measuring point, an additional uncertainty should be expected. An actual form deviation with a error limit of 0,9 µm is assumed. This leads to the following results.

Due to a capability ratio QMP of 26,74% in case of a process capability ratio limit QMP_max of 30%, the measurement process capability for production (without long-term analysis) is established. For further optimizing the measurement process, the manual measurement method for determining the form deviation was changed to an automated method. This leads to a error limit of 0,6 μm that is associated with the actu-

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al form deviation. The stability was observed in a long-term analysis and includes a error limit of 0,35 μm. This leads to the following uncertainty budget and overview of results.

Due to a capability ratio QMP of 22,34% in case of a process capability ratio limit QMP_max of 30%, the measurement process capability for production is established.

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Annex F.7 Compensation for Temperature Difference Calculating the standard uncertainty uT without correction of different linear expansions The nominal diameter of 85 mm shall be measured on a test part made of aluminium, however, without making any major compensation for temperature difference. A setting ring gauge made of steel is used for a comparison measurement. Temperatures of up to 30°C can occur at the workstation. There are not any precise information about the expansion coefficients of the test part and setting ring gauge available. Information about temperature influences Nominal dimension

85,00 mm

Length of the standard at 20° C (Ø setting ring gauge) yR

85,002 mm

Maximum temperature tMAX

30° C

Expansion coefficient of test part αOBJ

0,000024 1/K

Expansion coefficient of standard αR

0,0000115 1/K

Standard uncertainty from thermal expansion coefficient of test part uαOBJ

10% of αOBJ

Standard uncertainty from thermal expansion coefficient of standard uαR

10% of αR

According to these specifications, the measurement error is calculated by the formula B.6

∆y = 85,002 ⋅ (30 − 20 ) ⋅ (0,000024 − 0,0000115 ) = 0,0106 mm . Because of uncertain expansion coefficients, the uncertainty from other influence components in case of a temperature deviation of 10° C from the reference temperature of 10° C is calculated by formula B.5.

uREST = 85,002 ⋅ 102 ⋅ 0,000001152 + 102 ⋅ 0,00000242 = 0,0023 mm.

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According to formula B.7, these results lead to the error limit of

a = 0,0106 + 2 ⋅ 0,0023 = 0,0152 mm and, according to formula B.8, to a standard uncertainty from temperature of

uT =

0,0152 3

= 0,0088 mm .

In this case (assuming that uα = 0,1⋅ αOBJ ;R ), the standard uncertainty OBJ ;R can also be determined with the help of Table B.2. Using the value uT = 10,3 µm per 100 mm from the table, the following result is obtained (aside from little rounding differences):

uT = 10,3 ⋅

85,002 = 8,76 µm. 100

Calculating the standard uncertainty uT with correction of different linear expansions The uncertainty budget shows that the uncertainty component displayed above is too high. Therefore, the measurement results are corrected in order to reduce the uncertainty components to an acceptable level. In order to record the temperatures occurring during the measurement, a temperature measuring device is used that, according to manufacturer specifications, does not exceed a maximum deviation of ± 0,5° C. In case of the test part temperature of 28,2° C and the setting ring gauge temperature of 26,7° C, a difference of d = +0,014 mm was measured. This leads to a measured quantity value of Ø 85,016 mm. This measured value is corrected according to formula B.3.

y korr =

85,002 ⋅ (1+ 0,0000115 ⋅ (26,7 − 20)) + 0,014 1 + 0,000024 ⋅ (28,2 − 20)

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= 85,0058 mm.

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Since the standard uncertainty from the temperature measurement amounts to u ∆T = 0,5 3 = 0,2887 , a residual uncertainty remains according to OBJ ; R B.5 that represents the standard uncertainty from temperature that is now considerably smaller.

uT = 85,002 ⋅

6,72 ⋅ 0,000001152 + 8,22 ⋅ 0,00000242 + +0,00001152 ⋅ 0,28872 + 0,0000242 ⋅ 0,28872

= 0,0019 mm.

Conclusion:

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The advantage of avoiding complicated temperature measurements and compensations in case of high maximum temperatures is always gained on account of a relatively high (often too high) uncertainty component caused by temperature influences. For this reason, in most cases, the more time-consuming method is required, i.e. the temperatures occurring during the measurement must be determined and taken into account. Where possible, the application of modern, computer-based measuring instruments should be considered in test planning. These instruments perform and make most of the measurements and calculations that the users otherwise have to do themselves.

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Annex F.8 Inspection by Attribute without Critical Values A procedure for the visual inspection of semi-finished surfaces on die casting components requires that the capability of the measurement process is established and documented. 2 operators perform 3 repeated measurements on each of 40 semi-finished surfaces. The results of both operators are plotted on a matrix and compared. Then they are checked for symmetry using the Bowker test. The 95% quantile of the χ² distribution with 3 degrees of freedom is used as a critical value. The test results are displayed in the matrix below. Their evaluation is shown in the overview of results. Operator B

No. of repetitions

Result

Result

Result

mixed Result

Result Operator A mixed Result

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Thus, the test result exceeds the critical value of the 95% level of confidence, i.e. there is no symmetrical relation between the test results of the two operators. The procedure of the visual inspection is not suitable for semi-finished surfaces. In order to improve the visual inspection, a new catalogue of boundary samples is introduced and both operators repeat the entire test. This leads to the following matrix and overview of results. Operator B

No. of repetitions

Result

Result

Result

mixed Result

Operator A

Result mixed Result

² = 2,20 does not exceed the critical value of 7,81. A symmetrical relation between the test results of the two operators is proved. The capability of the visual inspection including a new catalogue of boundary samples is established.

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Annex F.9

Inspection by Attribute with Reference Values

The measurement process capability should be established and documented for a measurement procedure with one characteristic that can only be measured by using gauges. Information about attribute measurement process Nominal value

3,600 mm

Upper specification limit U

3,638 mm

Lower specification limit L Measurement QATTR_max

process

3,562mm capability

ratio

limit

30%

The information above specifies the characteristic. Two operators shall perform 2 repeated measurements on each of 20 reference parts. These inspections provide the following unsorted and sorted test results.

Unsorted test results

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Sorted test results

The following statistical values are calculated from the test results. Last test with agreement on negative result First test with agreement on positive result Last test with agreement on positive result First test with agreement on negative result

3,663 3,621 3,583 3,555

Ranges of the upper and lower conformance zones dU = 3,663 – 3,621 = 0,042 dL = 3,583 – 3,555 = 0,028 Average range d = (dU + dL) / 2 = (0,042 + 0,028) / 2 = 0,035 Uncertainty range and capability ratio UATTR = d / 2 = 0,035 / 2 = 0,0175 QATTR = 2 UATTR / TOL 100% = 2 0,0175 / 0,076 100% = 46,05 %

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Due to a capability ratio QATTR of 46,05% in case of a capability ratio limit QATTR_max of 30%, the capability of the measurement procedure using reference values is not established.

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11

Index of Formula Symbols Symbol MPE uAV

Term maximum permissible measurement error standard uncertainty from reproducibility of operator

uBI

standard uncertainty from measurement bias

uCAL

calibration standard uncertainty on a standard

uEV

standard uncertainty from maximum value of repeatability or resolution measuring system: max {uEVR, uRE} measurement process: max {uEVR, uEVO, uRE}

uEVO

standard uncertainty from repeatability on test parts

uEVR

standard uncertainty from repeatability on standards

uGV

standard uncertainty from reproducibility of measuring system

uIAi

standard uncertainty from interactions

uLIN

standard uncertainty from linearity

uMP

combined standard uncertainty on measurement process

uMS

combined standard uncertainty on measuring system

uMS_REST uOBJ

standard uncertainty from test part inhomogeneity

uRE

standard uncertainty from resolution of measuring system

uREST

standard uncertainty from other influence components not included in the analysis of the measurement process

uSTAB

standard uncertainty from stability of measuring system

uT

standard uncertainty from temperature

u(xi)

standard uncertainty

u(y)

combined standard uncertainty

UATTR

154

standard uncertainty from other influence components not included in the measuring system analysis

uncertainty range

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Symbol

Term

UMP

expanded measurement uncertainty (measurement process)

UMS

expanded measurement uncertainty (measuring system)

RE

resolution

Bi

bias

QMS

capability ratio (measuring system)

QMP

capability ratio (measurement process)

QMS_max

capability ratio limit (measuring system)

QMP_max

capability ratio limit (measurement process)

TOL

tolerance

TOLMIN-UMP minimum permissible tolerance of measurement process TOLMIN-UMS minimum permissible tolerance of measuring system k

coverage factor

a

variation limit

b

distribution factor

U

1)

upper specification limit U (specification limit that defines the upper limiting value)

L

1)

lower specification limit L (specification limit that defines the lower limiting value)

P 1)

test result, characteristic value

The GUM [22] or ISO 14253 [13] uses the formula symbol U for the expanded measurement uncertainty. However, new standards, such as ISO 3534-2 [9] refer to the upper specification limit as U. In order to avoid confusions in this document, the expanded measurement uncertainty is referred to as UMS where the measuring system is concerned and UMP when it is about the measurement process.

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Symbol Term UCL

upper control limit

LCL

lower control limit

Cg

capability index of measuring system

Cgk

minimum capability index of measuring system

Cp;real

real process capability index

sg

standard deviation

xm

reference quantity value of the standard

xmu

reference quantity value of the standard at the upper specification limit

xmm

reference quantity value of the standard in the centre of the specification

xml

reference quantity value of the standard at the lower specification limit

Cp;obs T ∆TOBJ

observed process capability index temperature temperature deviation of test part from 20° C

∆TR

temperature deviation of scale or standard from 20° C

αOBJ

thermal expansion coefficient of test part

αR

thermal expansion coefficient of scale or standard

yR

length of standard at a reference temperature of 20° C

ycorr

corrected measured quantity value

d

temperature difference between test part and standard

yi

measured quantity value

Y

measurement result (measured quantity value yi including the expanded measurement uncertainty UMP)

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Symbol Term N

number of standards (n = 1, ..., N)

K

number of repeated measurements (k = 1, ..., K) per standard

KB

class width

σ²

variance

xn

conventional true value for the n-th standard

yn

measured quantity value of the n-th standard

ynk

k-th of K measurements on the n-th of N standards

x

arithmetic mean of all conventional true values

y

arithmetic mean of all measured quantity values

εnk

deviation of the measured quantity value of the k-th of K measurements on the nth of N standards from its expected value

enk

residuals of the k-th of K measurements on the n-th of N standards

β0

y-intercept

βˆ 0

estimated y-intercept

β1

slope of the regression function

βˆ1

estimated slope of the regression function

1-α

z1-α / 2 f

t f ,1-α / 2

level of confidence quantile of standard normal distribution number of degrees of freedom quantile of Student t-distribution with f degrees of freedom

SS

sum of squares

MS

mean square

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12

Bibliography [1]

A.I.A.G. – Chrysler Corp., Ford Motor Co., General Motors Corp. th Measurement Systems Analysis, Reference Manual, 4 edition. Michigan, USA, 2010.

[2]

Deutscher Kalibrierdienst DKD-3: Angabe der Messunsicherheit bei Kalibrierungen. DKD bei der PTB, Braunschweig, 2002.

[3]

Deutscher Kalibrierdienst DKD-4: Rückführung von Mess- und Prüfmitteln auf nationale Normale. DKD bei der PTB, Braunschweig, 1998.

[4]

Dietrich, E. / Schulze, A. Measurement Process Qualification: Gage Acceptance and Measth urement Uncertainty According to Current Standards, 3 completely revised edition. Carl Hanser Verlag, Munich, 2011.

[5]

DIN - Deutsches Institut für Normung DIN 1319-1: Grundlagen der Messtechnik – Teil 1: Grundbegriffe. Beuth Verlag, Berlin, 1995.

[6]

DIN - Deutsches Institut für Normung DIN 1319-2: Grundlagen der Messtechnik – Teil 2: Begriffe für die Anwendung von Messgeräten. Beuth Verlag, Berlin, 1996.

[7]

DIN - Deutsches Institut für Normung DIN 1319-3: Grundlagen der Messtechnik – Teil 3: Auswertung von Messungen einer einzelnen Messgröße, Messunsicherheit. Beuth Verlag, Berlin, 1996.

158

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[8]

DIN - Deutsches Institut für Normung DIN 55319-3: Statistische Verfahren - Teil 3: Qualitätsfähigkeitskenngrößen zur Beurteilung von Messprozessen bei multivariat normalverteilten Messergebnissen. Beuth Verlag, Berlin, 2007.

[9]

DIN - Deutsches Institut für Normung ISO 3534-1 to 3534-3: Statistics – Vocabulary and symbols. Beuth Verlag, Berlin, 2006.

[10] DIN - Deutsches Institut für Normung ISO 9000:2005: Quality management systems – Fundamentals and Vocabulary. Beuth Verlag, Berlin, 2005. [11] DIN - Deutsches Institut für Normung DIN EN ISO 9001:2008: Qualitätsmanagementsysteme - Anforderungen. Beuth Verlag, Berlin, 2008. [12] DIN - Deutsches Institut für Normung EN ISO 10012:2003 Measurement management systems – Requirements for measurement processes and measuring equipment. Beuth Verlag, Berlin, 2004. [13] DIN - Deutsches Institut für Normung ISO/TS 14253-1: Geometrical product specifications (GPS). Inspection by measurement of workpieces and measuring equipment. Part 1: Decision rules for proving conformance or non-conformance with specifications. Beuth Verlag, Berlin, 1999. [14] DIN - Deutsches Institut für Normung Supplemetary sheet to ISO/TS 14253-1: Inspection by measurement of workpieces and measuring equipment. Guide to the estimation of uncertainty in GPS measurement, in calibration of measuring equipment and in production verification. Beuth Verlag, Berlin, 1999.

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[15] DIN - Deutsches Institut für Normung ISO/TR 14253-2: Geometrical product specifications (GPS) – Inspection by measurement of workpieces and measuring equipment. Part 2: Guide to the estimation of uncertainty in GPS measurement, in calibration of measuring equipment and in product verification. International Organization for Standardization, Geneva, 1998. [16] DIN - Deutsches Institut für Normung ISO/TS 15530-3:2009-07 Geometrical Product Specifications (GPS) – Coordinate measuring machines (CMM): Techniques for evaluation of the uncertainty of measurement - Part 3: Use of calibrated workpieces. Beuth Verlag, Berlin, 2000. [17] DIN - Deutsches Institut für Normung EN ISO/IEC 17000:2004 Conformity assessment – Vocabulary and general principles Beuth Verlag, Berlin, 2005. [18] DIN - Deutsches Institut für Normung EN ISO/IEC 17024:2003 Conformity assessment - General requirements for bodies operating certification of persons Beuth Verlag, Berlin, 2003. [19] DIN - Deutsches Institut für Normung EN ISO/IEC 17025:2005 General requirements for the competence of testing and calibration laboratories Beuth Verlag, Berlin, 2005.

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[20] DIN - Deutsches Institut für Normung DIN ISO 55350-12: Ausgabe: 1989-03 Begriffe der Qualitätssicherung und Statistik; Merkmalsbezogene Begriffe. Beuth Verlag, Berlin, 1989. [21] DIN - Deutsches Institut für Normung DIN ISO/IEC Guide 99:2007 International vocabulary of metrology (VIM). Beuth Verlag, Berlin, 2010. [22] DIN - Deutsches Institut für Normung ISO/IEC Guide 98-3 (2008): Guide to the expression of uncertainty in measurement (GUM:1995). International Organization for Standardization, Geneva, 2008. [23] DIN - Deutsches Institut für Normung ISO/TS 16949:2009-06 Vornorm: Qualitätsmanagementsysteme Besondere Anforderungen bei Anwendungen von ISO 9001:2008 für die Serien- und Ersatzteil-Produktion in der Automobilindustrie. Beuth Verlag, Berlin, 2009. [24] ISO – International Standard Organization ISO/WD 22514-7: Capability and performance – Part 7: Capability of Measurement Processes. Geneva, 2008. ®

[25] Q-DAS GmbH Leitfaden der Automobilindustrie zum „Fähigkeitsnachweis von Messsystemen“, Version 2.1. Weinheim, 2002.

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[26] VDA – Verband der Automobilindustrie VDA Band 6: Teil 1 - QM - Systemaudit - Grundlage DIN EN ISO 9001 und DIN EN ISO 9004-1. VDA, Frankfurt, 2003. [27] VDI/VDE/DGQ Richtlinie VDI/VDE/DGQ 2617, Blatt 7: Genauigkeit von Koordinatenmessgeräten - Kenngrößen und deren Prüfung - Ermittlung der Unsicherheit von Messungen auf Koordinatenmessgeräten durch Simulation. Beuth Verlag, Berlin, 2008. [28] VDI/VDE/DGQ Richtlinie VDI/VDE/DGQ 2618, Blatt 9.1: Prüfmittelüberwachung - Prüfanweisung für Messschieber für Außen-, Innen- und Tiefenmaße. Beuth Verlag, Berlin, 2006.

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13

Index coverage factor · 14, 37 coverage probability · 55 C-value · 47

A absolute measurement · 99 action limits · 71, 78 adjustment · 18 ANOVA · 16 ANOVA tables · 94 ARM · 35 attribute · 85 averaging · 105

D definitions · 13 D-optimum design · 35 D-optimum experimental design · 114

E

B

environment · 27, 48 evaluation method · 28 expanded · 68 expanded measurement uncertainty · 15, 29, 37, 38

bias · 16, 50 bibliography · 158

C calibration · 18 calibration uncertainty · 50, 52, 74 capability · 26, 43, 46 capability analysis · 49, 64 capability of measuring system · 56 capability of the measuring system · 20 capability of the measuring system · 29 capability ratios · 40, 68 characteristic · 16 classification · 81 CMM · 117 combined measurement uncertainty · 38 comparison measurement · 99 conformance zone · 23 conformity · 15 conformity assessment · 15, 29 control chart · 22, 71 control limit · 72 conventional true value · 17 correction · 60, 99, 102

F form deviation · 78 formula symbols · 154

G guidelines · 10 GUM · 10, 13

I influences · 26, 75 inspection by attribute · 151 inspection by attributes gauging · 17 inspection by variables measuring · 17 instrumental drift · 31 interactions · 69, 112

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intermediate measurement precision · 16 intermediate precision · 16

K k factors · 107

N

L

non-conformance zone · 24 not capable · 47

lack-of-fit · 61, 62 error limit · 37 limits · 42 linear expansion · 99 linearity · 53 linearity analysis · 60 linearity deviation · 50, 52, 76 long-term analysis · 32

M

O obsered process capability index · 46 ongoing review · 29, 71, 92 operator · 15, 28, 48, 65, 68 operator influence · 76 outlier · 31, 62

P

man · 28 material measures · 48, 58, 59 maximum permissible measurement error · 20 measured quantity value · 16 measurement error systematic · 30, 50, 52, 53, 75 measurement method · 28 measurement procedure · 28 measurement process · 20, 49, 64 measurement process capability · 20, 29 measurement process models · 79 measurement repeatability · 16, 50 measurement result · 16 measurement software · 82 measurement stability · 48, 71 measurement standard · 17, 27 measurement uncertainty · 11, 13 measuring equipment · 19, 27, 48 measuring instrument · 19 measuring system · 19, 27, 50

164

method of moments · 35 metrological traceability · 18 minimum tolerance · 44, 68 mounting device · 28 MPE · 43 MSA · 16

place of measurement · 65, 77 pooling · 69, 97

R random measurement errors · 30 reference quantity values · 86, 88 reference standard · 13, 27 reference temperature · 100 reference values · 149, 151 regression function · 50, 60, 72, 93 regression line · 60 repeatability · 16, 52, 53, 75 repeated measurements · 52, 54, 60 reproducibility · 32, 76 residual · 63 residual standard deviation · 60, 61 residuals · 93 resolution · 19, 50, 52, 53, 74

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S setting · 19 setting a working point · 108 small geometric elements · 81 small tolerances · 81 special measurement processes · 81 stability · 20, 48 stability of the measuring instrument · 20 standard · 52 standard measurement uncertainty · 14 combined · 14 standard normal distribution · 63 standard uncertainty · 14 combined · 14 standards · 10

T temperature · 48, 78 temperture influences · 98 test characteristic · 15 test part · 28, 65, 78 test parts · 48 testing · 15 thermal expansion coefficient · 99 time · 65, 78 tolerance · 21

true value · 17 Type 1 study · 54, 56, 75 Type 2 study · 65, 76 Type 3 study · 77 Type A evaluation · 34 ANOVA · 34 standard deviation · 34 Type B evaluation · 33, 36

U uncertainty budget · 14, 45 uncertainty component · 14 uncertainty components · 65 uncertainty range · 25, 85, 88

V validation · 21, 82 value of the characteristic · 16 verification · 21 vibrations · 48 VIM · 13

W working standard · 17

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14

Downloads

Since some graphics are reduced in size due to the format of this document, they are available for free download. Please use the following access data in order to download a PDF file of the graphics (that are listed below). Access data: www.vdaqmc.de/downloads user name: vda5ppeignung2a password: 2010vda52 Chap. 3.2 -

Fig. 1 - Page 22

Annex F.1 - Figure - Page 113

Chap. 4.1 -

Fig. 5 - Page 26

Annex F.2 - Figure - Page 116

Chap. 4.7 -

Fig. 7 - Page 41

Annex F.4 - Figure - Page 125

Chap. 4.10 - Fig. 9 - Page 46

Annex F.5 - Figure - Page 127

Chap. 5.2 -

Fig. 10 - Page 51

Annex F.5 - Figure - Page 129

Chap. 5.2.2.2 - Fig. 15 - Page 62

Annex F.5 - Figure - Page 130

Chap. 5.2.2.2 - Fig. 16 - Page 63

Annex F.6 - Figure - Page 134

Chap. 5.3 -

Fig. 19 - Page 66

Annex F.6 - Figure - Page 136

Cha. 9.3.1 -

Fig. 23 - Page 91

Annex F.6 - Figure - Page 138

Annex B -

Fig. 24 - Page 98

Annex F.6 - Figure - Page 141

Annex E -

Figure - Page 108

Annex F.8 - Figure - Page 149

Annex E -

Figure - Page 109

Annex F.8 - Figure - Page 150

Annex E -

Figure - Page 109

Annex F.9 - Figure - Page 151

Annex F.1 -

Figure - Page 111

Annex F.9 - Figure - Page 152

Annex F.1 -

Figure - Page 112

Annex F.9 - Figure - Page 153

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Quality Management in the Automotive Industry The current versions of the published VDA volumes about the quality management in the automotive industry (QAI) are found online under http://www.vda-qmc.de/en/. Our publications can be ordered from our website directly.

Reference: Verband der Automobilindustrie e.V. (VDA) Qualitäts Management Center (QMC) Behrenstraße 35, 10117 Berlin, Germany Telephone +49 (0) 30 897842 - 235, Telefax +49 (0) 30 897842 - 605 E-Mail: [email protected], Internet: www.vda-qmc.de

Forms: Henrich Druck + Medien GmbH Schwanheimer Straße 110, 60528 Frankfurt am Main Germany Telephone +49 (0) 69 9 67 777-158, Telefax +49 (0) 69 67 77-111 E-Mail: [email protected], Internet: www.henrich.de

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Quality Management in the Automotive Industry

5

Capability of Measurement Processes Capability of Measuring Systems Capability of Measurement Processes Expanded Measurement Uncertainty Conformity Assessment

nd

2

completely revised edition 2010, updated July 2011

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Capability of Measurement Processes

Capability of Measuring Systems Capability of Measurement Processes Expanded Measurement Uncertainty Conformity Assessment

Second completely revised edition 2010, up-dated July 2011 Verband der Automobilindustrie e.V. (VDA)

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ISSN 0943-9412 Printed: 07/2011 This version corresponds to the modified German version of July 2011 A change data sheet VDA 5 / 2011 versus VDA 5 / 2010, can be downloaded, access see page 166

Copyright 2011 by Verband der Automobilindustrie e.V. (VDA) Qualitäts Management Center (QMC) 10117 Berlin, Behrenstraße 35 Germany Overall production: Henrich Druck und Medien GmbH 60528 Frankfurt am Main, Schwanheimer Straße 110 Germany Printed on chlorine-free bleached paper Dokument wurde bereitgestellt vom VDA-QMC Internetportal am 18.08.2011 um 01:03

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Noncommittal VDA recommendation regarding standards The German Association of the Automotive Industry (Verband der Automobilindustrie e.V. - VDA) advises its members to apply the following recommendation regarding standards in implementing and maintaining QM systems. Exclusion of liability VDA Volume 5 is a recommendation that is free for anyone to use. Anyone using it has to ensure that it is applied correctly in each individual case. VDA Volume 5 considers the latest state of the art at the date of publication. The application of the VDA recommendation does not absolve users from their personal responsibility for their own actions. Users are acting at their own risk. The VDA and anyone involved in providing this VDA recommendation exclude liability for any damage. Anyone using this VDA recommendation is asked to inform the VDA in case of detecting any incorrect or ambiguous information in order that the VDA can fix possible errors. References to standards The individual standards referred to by their DIN standard designation and their date of issue are quoted with the permission of the DIN (German Institute for Standardization). It is essential to use the latest issue of the standards, which are available at Beuth Verlag GmbH, 10772 Berlin, Germany. Copyright This document is protected by copyright. Any use outside the strict limits stipulated by copyright law is prohibited without the consent of the VDA and is punishable by law. This applies particularly with regard to copying, translating, microfilming, storing and processing the document in electronic systems. Translations This document will also be translated into other languages. Please contact the VDA-QMC for information about the latest translations.

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Preface Different standards and guidelines contain requirements for estimating and considering the measurement uncertainty. In this regard, companies have to face various questions in implementing and certifying their quality management system. This document explains how to meet these various requirements. A work group of the automotive and supplier industry created VDA Volume 5. It applies to all parts of this branch of industry. The procedures described in this document are based on the ISO/IEC Guide 98-3 (Guide to the expression of uncertainty in measurement) (GUM) [22] and on ISO/TS 14253 (Inspection by measurement of work pieces and measuring equipment, Part 1: Decision rules for proving conformance or non-conformance with specifications) [13]. VDA Volume 5 also contains the well-established and widely used procedures of the MSA manual [1] that are used in order to evaluate and accept measuring equipment. It provides some information about the validation of measurement software as well. In order to ensure the functionality of technical systems, single parts and assemblies have to keep specified tolerances. The following aspects must be considered when determining the necessary tolerances in the construction process: • The functionality of the product must be ensured. • Single parts and assemblies must be produced in a way that they can be assembled easily. • For economic reasons, the tolerances should be as wide as possible, but for functionality reasons, it should be as narrow as necessary. • The expanded measurement uncertainty must be considered in statistical tolerancing. Due to the measurement uncertainty, the range around the specification limits does not allow for a reliable statement about conformance or nonconformance with specified tolerances. This might lead to an incorrect evaluation of measurement results. For this reason, it is important to consider the uncertainty of the measuring system and the measurement process as early as in the planning phase. VDA Volume 5 primarily refers to the inspection of geometrical quantities. Whether or not the approach explained in this document is suitable for measuring physical quantities must be checked in each individual case.

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Our thanks go to the following companies and, in particular, to the people involved for their commitment in creating this document: BMW Group, Munich Daimler AG, Untertürkheim Daimler AG, Sindelfingen GKN Driveline Offenbach KFMtec Methodenentwicklung, Stuttgart MAN Nutzfahrzeuge Aktiengesellschaft, Munich MQS Consulting, Oberhaid Q-DAS GmbH & Co. KG, Weinheim Robert Bosch GmbH, Stuttgart Volkswagen AG, Wolfsburg Volkswagen AG, Kassel VW Nutzfahrzeuge, Hanover

We also would like to thank everyone who provided their suggestions and helped us to improve this document.

Berlin, September 2010 Berlin, July 2011

Verband der Automobilindustrie e. V. (VDA)

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Table of Contents

Page

Preface 1

Standards and Guidelines

5 10

2

Benefits and Field of Application

11

3 3.1

Terms and Definitions General Terms and Definitions

13 13

3.2

Proof of Conformance or Non-conformance with Tolerances according to ISO/TS 14253 [13] 22

4 4.1

General Procedure for Establishing the Capability of Measurement Processes 26 Influences Causing the Uncertainty of Measurement Results 26

4.2

General Information

29

4.3

Specific Approaches

30

4.3.1

Measurement Errors

30

4.3.2

Long-term Analysis of Measurement Process Capability

32

4.3.3

Reproducibility of Identical Measuring Systems

32

4.4

Standard Uncertainties

33

4.4.1

Type A Evaluation (Standard Deviation)

34

4.4.2

Type A Evaluation (ANOVA)

34

4.4.3

Type B Evaluation

36

4.4.3.1

Type B Evaluation: Expanded Measurement Uncertainty UMP Known 37

4.4.3.2

Type B Evaluation: Expanded Measurement Uncertainty UMP Unknown

37

4.5

Combined Standard Uncertainty

38

4.6

Expanded Measurement Uncertainty

38

4.7

Calculation of Capability Ratios

40

4.8

Minimum Possible Tolerance for Measuring Systems / Measurement Processes

44

4.9

Uncertainty Budget

45

4.10

Capability of the Measurement and Production Processes

46

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Dealing with Not Capable Measuring Systems / Measurement Processes

47

5 5.1

Measurement Process Capability Analysis Basic Principles

49 49

5.2

Capability Analysis of a Measuring System

50

5.2.1

Resolution of the Measuring System

53

5.2.2

Repeatability, Systematic Measurement Error, Linearity

53

5.2.2.1

Estimating the Systematic Measurement Error and Repeatability according to the “Type 1 Study”

54

5.2.2.2

Linearity Analysis with Correction on the Measuring Instrument

60

5.3

Measurement Process Capability Analysis

64

5.3.1

Example for Determining the Uncertainty Components of the Measurement Process 69

6 6.1

Ongoing Review of the Measurement Process Capability 71 General Review of the Measurement Stability 71

6.2

Correcting the Regression Function

72

7 7.1

Practical Guidance to Determining Typical Standard Uncertainties Overview of Typical Measurement Process Models

74 79

8 8.1

Special Measurement Processes Measurement Process with Small Tolerances

81 81

8.2

Classification

81

8.3

Validation of Measurement Software

82

9 9.1

Capability Analysis of Attribute Measurement Processes 85 Introduction 85

9.2

Capability Calculations without Using Reference Values

9.3

Capability Calculations Using Reference Values

88

9.3.1

Calculation of the Uncertainty Range

88

9.3.2

Ongoing Review

92

10

Appendix

93

4.11

8

86

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Annex A

Statistical Background of the Measurement Process Capability Analysis

93

Annex B

Estimation of Standard Uncertainties from Temperature 98

Annex C

Reducing the Measurement Uncertainty by Repeating and Averaging Measurements 105

Annex D

k Factors

107

Annex E

Setting Working Point(s)

108

Annex F

Calculation Examples

110

11

Index of Formula Symbols

154

12

Bibliography

158

13

Index

163

14

Downloads

166

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1

Standards and Guidelines

Relevant quality management standards and guidelines require knowledge of the measurement uncertainty or a capability analysis of the measuring and test equipment (qualification of the measuring and test equipment for the respective measurement process). The documents listed in Table 1 contain requirements for measurement processes.

Aim Implementation of QM systems

International/national standards and documents • DIN EN ISO 9000ff [10][11];

Industry standards • VDA Volume 6, Part 1[26]

• ISO 10012 [12]; • EN ISO/IEC 17025 [19]; • ISO/TS 16949 [23]

Estimation of the measurement uncertainty

Metrology, general: • DIN 1319 [5][6][7]; • ISO/IEC Guide 98-3 (GUM) [22]

• standards of technical associations • DKD-3 [2]

Dimension measurement: • attachment 1 to ISO 14253-1 [14] Calculation of the capability of measuring instruments and measuring equipment Consideration of the measurement uncertainty

Table 1:

• QS-9000/ MSA [1] • DIN 55319-3 [8]

• corporate standard

• ISO/WD 22514-7 [24] • ISO/TS 14253-1 [13]

• QS-9000/ MSA [1] • corporate standards

Aims specified in certain standards, recommendations and guidelines to the evaluation of measuring equipment

The aim of VDA Volume 5 is to summarize the requirements and procedures of the existing standards and guidelines in order to gain a standardized and practice-oriented model for the estimation and consideration of the expanded measurement uncertainty. The methods and capability analysis (see MSA [1]) established in practice may be integrated where applicable. Table 14 provides answers to typical questions regarding the estimation of standard measurement uncertainties and the expanded measurement uncertainty.

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2

Benefits and Field of Application

Measuring systems and measurement processes require an adequate and comprehensive evaluation. This evaluation has to include the consideration of influencing quantities such as the calibration uncertainty on the reference standards and its traceability to a national or an international measurement standard, the influence of the test part or the long-term stability of a measuring instrument in the measurement process. If the capability of a measurement process is not established, measurement processes that are “not capable“ might be released. This could cause high consequential costs for corrective action and for the on-going review of a production process using SPC. Moreover, an inspection of the measuring systems could lead to discussions and additional, more complex inspections. The benefits from a qualified measurement process are great, because reliable and correct measurement results form the basis of important decisions, such as whether • to release or not to release a manufacturing device or measuring equipment. • to take or not to take corrective action in a running production process. • to accept or to reject a product. • to deliver, to rework or to scrap a product. Furthermore, in the case of product liability, it is required to give proof of the capability of the measurement processes used in order to manufacture and release the product. If this proof cannot be provided, the measurement results, that the evaluation of the products is based on, will always be contested. In the end, it is important to know that the expression of the measurement uncertainty is not a negative criterion or a deficit. It describes the actual quality or safety of a measurement result. For this reason, the measurement uncertainty is not referred to as “measuring error” in this document, as is often the case in literature. The measurement uncertainty is a piece of additional information in order to complete the measurement result. It must not be mistaken for an incorrect measurement result. VDA Volume 5 refers to repeatable processes measuring geometrical characteristics, such as the measurement of lengths and angles.

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Its applicability to destructive tests, rapidly changing measured quantity values or other physical quantities has not been validated and must be verified in each individual case. In addition, this document describes practical procedures in order to make a measurement systems analysis and to calculate the measurement uncertainty of measurement processes. It deals with the following issues: •

capability of measuring systems

•

short-term evaluation of the capability of entire measurement processes (with and without the influence of the test parts‘ form deviation, acceptance of measuring systems (measuring instruments), comparison of several places of measurement, measuring systems for the same measurement tasks)

•

long-term analysis of the capability of entire measurement processes over a significant period (e.g. for several days)

•

determination of the expanded measurement uncertainty in order to consider information about conformity according to ISO/TS 14253 Part 1 [13]

•

ongoing evaluation of the capability of a measurement process (stability of a measuring instrument)

It is also about specific features, such as •

test characteristics with narrow tolerances

•

classifications.

Within the quality management system, it is important to determine the field of application of this document, i.e. the processes or characteristics it applies to. A schematic approach helps to reach the reproducibility of the test results and facilitates its application in practice for users. This document is an enhanced version of the VDA Volume 5 “Capability of Measurement Processes“, 2003 edition. Its basic approach is to compare the measurement uncertainty or components of it, to the tolerance to be tested and to use this ratio as evaluation criterion. The procedures of the MSA manual (Measurement Systems Analysis) [1] established in practice can be included.

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3

Terms and Definitions

3.1

General Terms and Definitions

The following sections define the most important terms used in this document. Moreover, the terms and definitions according to ISO 3534-1 [9], ISO 10012 [12], VIM (International vocabulary of metrology) [21], ISO/IEC Guide 98-3 (GUM) [22], ISO/TS 14253 [13] and DIN 1319 [5] [6] [7] are applied. The definitions of most of the following terms are taken from standards (see reference). Colloquially, some other expressions are often used for some of the terms defined in this chapter. These expressions are added in parentheses. They are also used in the text. Measurement uncertainty [22] Parameter, associated with the result of a measurement that characterizes the dispersion of the values that could reasonably be attributed to the measurand. Note 1:

The parameter may be, for example, a standard deviation (or a given multiple of it), or the half-width of an interval having a stated level of confidence.

Note 2:

Uncertainty of measurement comprises, in general, many components. Some of these components may be evaluated from the statistical distribution of the results of series of measurements and can be characterized by experimental standard deviations. The other components, which can also be characterized by standard deviations, are evaluated from assumed probability distributions based on experience or other information.

Note 3:

It is understood that the result of the measurement is the best estimate of the value of the measurand and that all components of uncertainty, including those arising from systematic effects, such as components associated with corrections and reference standards, contribute to the dispersion.

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Standard uncertainty u(xi) [22] (standard measurement uncertainty or uncertainty component) Uncertainty of the result of a measurement expressed as a standard deviation. Uncertainty budget (for a measurement or calibration) Table summarizing the results of the estimations or statistical evaluations regarding the uncertainty components contributing to the uncertainty of a measurement result (see Table 5). Note 1:

The uncertainty of a measurement result is only clear if the measurement procedure (including the test part, measurand, measurement method and conditions of measurement) is defined.

Note 2:

The designation “budget” is associated with numerical values attributed to the uncertainty components, their combinations and extension based on the measurement procedure, the conditions of measurement and assumptions.

Combined standard uncertainty u(y) [22] (combined standard measurement uncertainty) Standard uncertainty of the result of a measurement when that result is obtained from the values of a number of other quantities, equal to the positive square root of a sum of terms, the terms being the variances or covariances of these other quantities weighted according to how the measurement result varies with changes in these quantities.

Coverage factor k [22] Numerical factor used as a multiplier of the combined standard uncertainty in order to obtain an expanded uncertainty (see Table 4 and Annex D). UMS or UMP = k · u(y)

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Expanded measurement uncertainty (expanded uncertainty) [22] Quantity defining an interval about the result of a measurement that may be expected to encompass a large fraction of the distribution of values that could reasonably be attributed to the measurand. Note 1:

The fraction may be viewed as the coverage probability or level of confidence of the interval.

Note 2:

To associate a specific level of confidence with the interval defined by the expanded uncertainty requires explicit or implicit assumptions regarding the probability distribution characterized by the measurement result and its combined standard uncertainty. The level of confidence that may be attributed to this interval can be known only to the extent to which such assumptions may be justified.

Remark:

The GUM [22] and ISO/TS 14253 [13] use the formula symbol U for the expanded measurement uncertainty. The latest standards, such as 3534-2 [9], refer to the upper tolerance limit as U. In order to avoid confusions, this document uses the symbol UMS for the expanded measurement uncertainty where the text refers to a measuring system and UMP where the text refers to a measurement process.

Testing (conformity assessment) [17] Determining one or more characteristics on an object included in the conformity assessment, according to a certain procedure. Conformity [10] Fulfilment of a requirement. Operator [18] Person possessing the relevant professional and personal qualifications in order to conduct an inspection and evaluate the results. Test characteristic [20] Characteristic the inspection is based on.

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Characteristic [21] Distinguishing feature. Value of the characteristic (measured quantity value) yi [20] Form of the value attributed to the characteristic. Measurement result (result of measurement) Y [21] Set of quantity values being attributed to a measurand together with any other available relevant information. Note:

A measurement result is generally expressed as a single measured quantity and a measurement uncertainty Y = y i ± U MP . If the measurement uncertainty is considered negligible for some purpose, the measurement result may be expressed as a single measured quantity value. In many fields, this is the common way of expressing a measurement value.

Bias / Bi [21] Estimate of a systematic measurement error. MSA [1] MSA refers to Measurement Systems Analysis. The MSA manual presents guidelines of the QS-9000 for the assessment and acceptance of measuring equipment. ANOVA ANOVA (Analysis of Variance) represents a mathematical approach in order to determine variances. Based on these variances, standard uncertainties can be estimated. Measurement repeatability (repeatability) [21] Measurement precision under a set of repeatability conditions of measurement. Intermediate measurement precision (intermediate precision) [21] Measurement precision under a set of intermediate precision conditions of measurement.

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Inspection by variables (measuring) Determination of a specific value of a measurand as a multiple or a component of an item or of a specified reference system. Measuring means to draw a quantitative comparison between the measurand and the reference value by using a measuring instrument or measuring equipment. Inspection by attributes (gauging) Comparison of a test part to a gauge in order to find out whether a specified limit is exceeded. The actual deviation of the tested quantity from the nominal quantity value is not determined. True quantity value (true value) [21] Value consistent with the definition of an observed, specific quantity. Note 1:

This value would be obtained by a perfect measurement.

Note 2:

True values are by nature indeterminate.

Conventional true value (of a quantity) [22] Value attributed to a particular quantity and accepted, sometimes by convention, as having an uncertainty appropriate for a given purpose. Note 1:

Conventional true value is sometimes called assigned value, best estimate of the value, conventional value or reference value.

Note 2:

Frequently, a number of results of measurements of a quantity are used to establish a conventional true value.

Measurement standard [21] Realization of the definition of a given quantity, with stated quantity value and associated measurement uncertainty used as a reference. Working measurement standard (working standard) [21] Measurement standard that is used routinely to calibrate or verify measuring instruments and measuring systems.

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Calibration [21] Operation that, under specified conditions, in a first step, establishes a relation between the quantity values with measurement uncertainties provided by measurement standards and corresponding indications with associated measurement uncertainties and, in a second step, uses this information to establish a relation for obtaining a measurement result from an indication. Note:

Calibration should not be confused with adjustment of a measuring system, often mistakenly called “self-calibration“.

Remark:

Comparison measurement taken under specified conditions between a more precise calibration device and the object to be calibrated in order to estimate the systematic measurement error.

Adjustment [21] Set of operations carried out on a measuring system so that it provides prescribed indications corresponding to given values of a quantity to be measured. Note 1:

Adjustment of a measuring system should not be confused with calibration, which is a prerequisite for adjustment..

Note 2:

After an adjustment of a measuring system, the measuring system must usually be recalibrated.

Remark:

Elimination of the systematic measurement error of the object to be calibrated are estimated in the calibration. Adjustment includes all actions required in order to minimize the deviation of the final indication.

Metrological traceability [21] and [3] Property of a measurement result whereby the result can be related to a reference through a documented unbroken chain of calibrations, each contributing to the measurement uncertainty.

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Setting Setting means to set measuring systems to a measure referring to material measures. If the aim of this operation is a zero indication, it is referred to as zero setting. Remark:

Setting means to transfer the calibrated actual value of the working measurement standard (material measure) to the measuring instrument under real operating conditions. Users make their measuring instruments ready for operation on site. Adjustment minimizes systematic measurement errors.

Measuring instrument [21] Device used for making measurements, alone or in conjunction with one or more supplementary devices. Note 1:

A measuring instrument that can be used alone is a measuring system.

Note 2:

A measuring instrument may be an indicating measuring instrument or a material measure.

Measuring equipment [10] Measurement instrument, software, measurement standard, reference material or auxiliary apparatus or combination thereof necessary to realize a measurement process. Resolution [21] The smallest change in a quantity being measured that causes a perceptible change in the corresponding indication. Measuring system [21] Set of one or more measuring instruments and often other devices, including any reagent and supply, assembled and adapted to give information used to generate measured quantity values within specified intervals for quantities of specified kinds.

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Capability of the measuring system Qualification of the measuring system for a specific measurement task exclusively taking into account the required accuracy of measurement (measurement uncertainty UMS) (see Chapter 4.7). Maximum permissible measurement error (error limit) MPE [21] Extreme value of measurement error, with respect to a known reference quantity value, permitted by specifications or regulations for a given measurement, measuring instrument, or measuring system. Note:

Usually, the term “maximum permissible errors“ or “limits of error” is used where there are two extreme values.

Measurement process [21] Interaction of interrelated operating resources, actions and influences creating a measurement. Note:

Operating resources can be both, human and materials.

Measurement process capability Qualification of the measurement process for a specific measurement task exclusively taking into account the required accuracy of measurement (expanded measurement uncertainty UMP) (see Chapter 4.7). Remark:

In general, the measuring system or measurement process capability analysis is a short-term evaluation. Especially in case of new measuring systems or measurement processes, the stability of a measuring instrument should be determined over a significant period and considered in order to prove capability.

Stability of a measuring instrument (stability) [21] Property of a measuring instrument, whereby its metrological properties remain constant in time. Note:

20

Stability may be quantified in several ways:

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Example 1:

In terms of the duration of a time interval over which a metrological property changes by a stated amount.

Example 2:

In terms of the change of a property over a stated time interval.

Remark:

Inspection of the stability must be demonstrated by means of an ongoing review of the capability of the measurement process (see Chapter 6).

Specified Tolerance [9] Difference between the upper specification limit U and lower specification limit L. Verification [21] Provision of objective evidence that a given item fulfils specified requirements. Example 1:

Confirmation that a given reference material as claimed is homogeneous for the quantity value and measurement procedure concerned, down to a measurement portion having a mass of 10 mg.

Example 2:

Confirmation that a target measurement uncertainty can be met.

Validation [21] Verification, where the specified requirements are adequate for an intended use. Example 1:

A measurement process must be determined with sufficient accuracy due to its interpretation of the “diameter” level. Validation ensures the capability of the measurement process needed for the specified size of the diameter (e.g. nominal value) and the demanded tolerance.

Example 2:

see Chapter 8.3

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Control chart Control chart, also referred to as quality control chart or QCC, is applied to statistical process control. A QCC generally consists of a “level” path and a “variation” path together with specified action limits. Statistical values such as sample means and sample standard deviations are plotted on the respective path of the QCC.

3.2

Proof of Conformance or Non-conformance with Tolerances according to ISO/TS 14253 [13]

Part 1 of ISO/TS 14253 establishes the rules for determining when the characteristics of a specific work piece or measuring equipment are in conformance or non-conformance with a given tolerance (for a work piece) or limits of maximum permissible errors (for measuring equipment), taking into account the uncertainty of measurement.

increasing measurement uncertainty UMP

It also gives rules on how to deal with cases where a clear decision (conformance or non-conformance with specification) cannot be taken, i.e. when the measurement result falls within the uncertainty range (see Figure 1) that exists around the tolerance limits. phase U specification (construction)

L

conformance zone

non-conformance zone

non-conformance zone

verification uncertainty range

uncertainty range

phase (production)

work piece tolerance outside the tolerance

Figure 1:

22

within the tolerance

outside the tolerance

Uncertainty ranges and conformance or non-conformance zones

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Conformance Fulfilment of specified requirements. Conformance zone Specification zone reduced by the expanded uncertainty of measurement UMP (Figure 2). Note:

The specification is reduced by the expanded uncertainty of measurement UMP at the upper and lower specification limits. In case of characteristics with a one-sided specification, this reduction does not apply to the natural boundary side.

Proof of conformance If the measurement result Y (measured quantity value yi associated with the expanded measurement uncertainty UMP) is lying within the specification zone, the conformance with the tolerance is proved and the product can be accepted. measurement result Y

UM P

U MP

measurement value yi

tolerance

L lower tolerance limit

Figure 2:

U upper tolerance limit

Proof of conformance with the tolerance

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Non-conformance Non-fulfilment of a specified requirement. Non-conformance zone Zone(s) outside the specification zone extended by the expanded uncertainty of measurement UMP (Figure 1). Note:

The specification is extended by the expanded uncertainty of measurement UMP at the upper and lower specification limit. In case of characteristics with a one-sided specification, this reduction does not apply to the natural boundary side.

Proof of non-conformance Non-conformance with the tolerance is proved when the measurement result Y (measured quantity value yi associated with the expanded measurement uncertainty UMP) is lying beyond the specification zone (Figure 3). In this case, the work piece must be rejected. measurement result Y

UM P

tolerance

L lower tolerance limit

Figure 3:

24

U MP

measurement value yi

U upper tolerance limit

Proof of non-conformance with the tolerance

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Uncertainty ranges Areas near the specification limits where conformance or non-conformance cannot clearly be determined because of the measurement uncertainty (Figure 1). When the measurement result Y (measured quantity value yi associated with the expanded measurement uncertainty UMP) includes one of the specification limits, neither conformance or non-conformance can be proved (Figure 4). Note 1:

Uncertainty ranges are symmetrical to the specification limits.

Note 2:

As a result, work pieces can neither be automatically accepted nor rejected. For such “dead end cases”, it is advisable to follow the rule below:

Reduce the uncertainty of measurement and thereby reduce the uncertainty range in order that conformance or non-conformance can clearly be demonstrated. Mutual agreement between customers and manufacturers: measurement result Y

UM P

tolerance

L lower tolerance limit

Figure 4:

U MP

measurement value yi

U upper tolerance limit

Conformance or non-conformance with the tolerance can be proved

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4

General Procedure for Establishing the Capability of Measurement Processes

Inspections for series production control and conformity assessments require characteristics that are identified correctly as characteristics in conformance, i.e. “o.k.” (within the specification limits), or in non-conformance, i.e. “n.o.k.” (beyond the specification limits), with the tolerance. It is important to consider the measurement error caused by the variation of the production process as well as errors caused by the measurement process. Measurement errors caused by the measurement process lead to an uncertain measurement result and thus to dubious decisions. Errors must be known and can only be accepted to a certain degree relating to the specified tolerance of the inspection. 4.1

Influences Causing the Uncertainty of Measurement Results

Influences caused by measuring systems, operators, test parts, environment, etc. usually affect the measurement result (see Figure 5) as random errors. Evaluation Method

Object

Man

Mathemat. models

Material Motivation

Qualification

Care

Calibration/ justification

Measuring range tactile touch

Time/cost Stability

Measuring points layout

not recorded bias

Measurement Procedure

Figure 5:

26

Setting uncertainty

Resolution

Gage

Mounting Fixture

Surface texture

Stability

random measurement deviations

Capacity Measuring points total

Vibrations Soiling

Measurement Result

Sensibility

contact-free

Illumination Humidity

Statistical method

Accessibility

Voltage Electricity

Temperature

Computer application

Surface

Discipline

Psychical constitution

Pressure

Measurement value composition

Shape Physical constitution

Environment

Shape Location

Position

Type of master Shape/Position

Measurement stability

Master

Important influences on the uncertainty of measurement results

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The following sections provide some examples of frequently recurring and important influence components that are described in detail in Chapter 5 and Table 14. Measurement standard / reference standard Depending on the quality of the measurement standard, it could lead to a considerable proportion of the uncertainty of the measurement result. The calibration certificate normally contains the respective uncertainty. The traceability of the standard used must be demonstrated. Measuring equipment / measuring system Important influence components associated with the measuring system are •

resolution

•

reference standard

•

setting to one or several test parts

•

linearity deviation / systematic measurement error

•

measurement repeatability

Environment Important influence components of the environment affecting the measurement process are •

temperature

•

lighting

•

vibrations

•

contamination

•

humidity

The influence of temperature variations on a test part, measuring system and clamping device are particularly significant in terms of environmental conditions. In case of measurements of lengths, this fact leads to different measurement results when the temperature changes. Table 11 and Annex B provide recommendations for the determination of the standard measurement uncertainty from temperature.

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Human / operator Influences of operators leading to the uncertainty of measurement results are caused by the different qualifications and skills of operators in taking measurements. • different measuring forces • reading errors because of parallaxes • physical and psychological constitution of the operator • qualification, motivation and care Test part Influences from test parts can be detected when, for example, the same characteristic is measured at different points on the test part. It results from, for example: • geometrical deviations (form deviations and changes in the surface texture) • material properties (e.g. elasticity) • lack of inherent stability Measurement method / measurement procedure The way a measurement is taken or the selected sampling strategy has an impact on the measurement result. Even the applied mathematical procedures for determining a measured quantity value are influencing the result. Mounting device If measuring instruments are built into installations, they will also affect the measurement result. Evaluation method The mathematical and statistical procedures used for evaluation (e.g. elimination of detected outliers or filtering) can have an effect on the result.

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4.2

General Information

The evaluation of measurement processes and the consideration of the measurement uncertainty are based on the following table (Table 2). Input information

Description

Information about the measuring system, the test characteristic and about the measurement standards (references) Information about the measurement process and the test characteristic including all uncertainty components to be considered

Measuring system capability analysis

Measurement process capability analysis

Result Expanded measurement uncertainty UMS capability ratio QMS (see Chapter 5.2) Expanded measurement uncertainty UMP capability ratio QMP (see Chapter 5.3)

Information about the test characteristic and the corresponding expanded measurement uncertainty UMP

Conformity assessment including the expanded measurement uncertainty

Conformance or nonconformance zone (see ISO/TS 14253 [13])

Information from measuring system, measurement process and about the test characteristic

Ongoing review of the capability of the measurement process

Control chart including the calculated action limits (see Chapter 6)

Table 2:

General procedures for establishing the capability of measurement processes

In order to prove the capability of a measurement process, all relevant uncertainty components affecting the measurement result must be considered. Moreover, the specifications of the test characteristic must be known in order to establish the capability of the measuring system and in order to prove the capability of the measurement process. A measurement process capability analysis requires the estimation of the expanded measurement uncertainty UMP. The capability ratio QMP is used as an evaluation criterion. The value of the expanded measurement uncertainty UMP is available for consideration in decision rules for proving conformance or non-conformance according to ISO/TS 14253 Part 1 [13].

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Ongoing monitoring provides proof of the stability of a measuring instrument and shows long-term influences. The following sections describe the single procedures. 4.3

Specific Approaches

4.3.1

Measurement Errors

measured value

Measurement errors in a measurement process consist of known and unknown systematic errors from a number of different sources and causes. In German, the traditional term “measuring error” has been replaced by the term “measurement deviation” since the publication of DIN 1319:1995. In case of measuring instruments or measuring systems, the permissible systematic errors prescribed by different standards and guidelines (e.g. VDI/VDE/DGQ 2618 ff [28]) are referred to as maximum permissible error or error limit.

1 Outlier 2 Dispersion 1 3 Dispersion 2 4 Systematic error 1 5 Systematic error 2 6 True value

1 3

4

5

2

6 1 time

Figure 6:

Measurement errors in results of measurements [13]

Different types of measurement errors (see Figure 6) show up in measurement results: • random measurement errors Random errors are caused by non-controlled random influence factors. They may be characterized by the standard deviation and the type of distribution (see Dispersion 1 and 2 in Figure 6).

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• systematic measurement errors (known, unknown) Systematic errors (see Chapter 5.2.2) may be characterized by size and sign (+ or –): Bi = yi – true value (6) see Figure 6 The difference between the reference value of a measurement standard and the mean of the measured values often form the basis for calculating the systematic measurement error:

Bi = xg - xm xg

arithmetic

mean

of

the

measured

xm

reference value of the measurement standard

values

Where measurement errors are not regarded as systematic, the cause of the measurement error has not been sought for economic and complexity reason or the resolution is inadequate (e.g. %RE greater 5% of the specification; see Chapter 5.2.1). Remark:

Bias is not regarded as a constant but a random variable.

• instrumental drift Drift is caused by a systematic influence of non-controlled influence factors. It is often a time effect or a wear effect. Drift may be characterized by change per unit time or per amount of use. Instrumental drifts characterized by change per unit time must be recorded in a “long-term experiment” (over several days) prior to the first application of the measuring instrument and the drifts have to be considered in series production (e.g. in the form of an instruction: “switch on measuring instrument 20 minutes before use”). If required, instrumental drifts caused by wear effects must be assessed by reviewing the stability of the measuring instrument (e.g. control chart). • outlier Outliers are caused by non repeatable incidents in the measurement. Noise – electrical or mechanical (e.g. voltage peaks and vibrations) – may result in outliers. A frequent reason for outliers is human mistakes as reading and writing errors or mis-handling of measuring equipment. Outliers are impossible to characterize in advance, but they might occur in an experiment.

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Remark 1:

Frequently applied methods to determine the capability of measuring equipment include consideration of the systematic measurement error with regard to a measurement standard representing the true value. In many cases, however, the measurement standards used in production (working standards) are not identical to the test parts measured in series production. This could lead to unexpected measurement errors. In order to ensure that this errors are sufficiently minor, some representative test parts should be measured by means of a superior measurement procedure (e.g. prior to release). The results are compared and evaluated. The reproducibility of the measurement method is crucial.

Remark 2:

Production-related measuring instruments are often based on comparison measurements. Setting an instrument with the help of a working standard means correcting the systematic measurement error. A repeatability test using the same working standard normally leads to a smaller bias.

Remark 3:

Further measurement errors could occur in measurements at several measuring points and where different measuring systems or measurement procedures are used for one measurement task. In order to guarantee reproducible measurement results for all systems and procedures used, these errors must be analyzed in experiments.

4.3.2

Long-term Analysis of Measurement Process Capability

The known procedures for capability analyses and the capability of measuring systems and measurement processes are conducted over a period of several minutes up to several hours. However, the results are only “shortterm conclusions” and do not give any information about the long-term behaviour of the determined values. In order to gain profound information, the required inspections should be made several times over a reasonable, significant period. For further information about the estimation of uncertainty components see Table 14. 4.3.3

Reproducibility of Identical Measuring Systems

In many cases, several identical but independent measuring systems are used for measurement processes with the same measurement task. An alternative is to combine the identical, independent measuring systems into an overall measuring system for a specific measurement task. Each one of these individual measuring systems is regarded as separate measurement process. The aim of this analysis is to ensure the reproducibility of the single measuring systems by means of the variation and the measurement error. It is im-

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portant to inspect reference standards and parts. For further information about the estimation of uncertainty components see Table 14. 4.4

Standard Uncertainties

The GUM [22] “Guide to the expression of uncertainty in measurement” describes how to determine the measurement uncertainty specific to the respective measurement task. The standard uncertainties for every relevant influence factor are estimated using the mathematical model of the measurement process. Standard uncertainties quantify the single uncertainty components. According to the law of propagation of uncertainty, sensitivity coefficients are partial derivatives of the respective equation of the measurement model with regard to each single influence factor. An uncertainty budget summarizes standard uncertainties, associated sensitivity coefficients and the calculated combined and expanded measurement uncertainties. In the practice of industrial applied metrology, a special case of mathematical model (sum/difference or product/quotient) is assumed where the sensitivity coefficients equal “1“. This leads to a simple quadratic addition of the uncertainties (see Chapter 4.5). Remark:

Complex, technical interactions (such as wear, contamination, manufacturer’s specifications, form deviations, positioning accuracy, vibrations, etc.) that are hard to express mathematically are considered in the experiment in the form of a sum result.

The standard uncertainty •

u ( xi ) can be estimated by

the statistical evaluation of series of measurements Type A evaluation

or by •

the use of available information

Type B evaluation

The standard uncertainties estimated by means of the Type A and Type B evaluations are equal.

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4.4.1

Type A Evaluation (Standard Deviation)

In the simplest case, the standard deviation sg of n individual observations is calculated from a series of n observations obtained under the same specified conditions of measurement: n

sg =

∑ (x

- x)

2

i

i =1

n -1

In order to determine the standard deviation sg, n = 25 repeated measurements are recommended. This experiment is generally only conducted once in the estimation of measurement uncertainty. The standard deviation will be considered in the measurement uncertainty budget in the form of the standard measurement uncertainty u(xi) if, as is usual in practice, the measurement result is obtained in one measurement only.

u ( xi ) = sg A lower value for u(xi) is achieved by taking several repeated measurements with the sample size n∗ > 1

u ( xi ) =

sg n∗

as the standard measurement uncertainty of the mean of all the sample values (see Annex C). 4.4.2

Type A Evaluation (ANOVA)

In addition to the procedures described here for determining only one uncertainty component u(xi) of an influence factor, there is also a statistical technique used to identify and quantify the effects of several influence factors in an experiment. This procedure has been applied to capability analysis according to the MSA manual (Measurement Systems Analysis [1]) for years. In order to calculate the %GRR (Gage Repeatability & Reproducibility), the operator and equipment variation is estimated in an experiment (e.g. 3 operators measure each of 10 test parts twice: 3 · 10 · 2 = 60 measurements).

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In this case, the method of ANOVA (Analysis of Variance) is used as described Annex A. Remark:

The MSA manual [1] describes the method of ANOVA and the Average Range Method (ARM). Under statistical considerations, the method of ANOVA should be preferred to the ARM, the more so as the method of ANOVA also evaluates interactions. The method of ANOVA is indeed more complex in a mathematical sense, but the use of specific computer software makes its application easy.

In the same experiment, further influence factors, such as the uncertainty from test parts or different measuring systems can be evaluated, as is strongly recommended in Chapter 3.4.1 of the ISO/IEC Guide 98-3 (GUM) [22]. However, each additional influence factor increases the effort for this experiment considerably. In case of the example described above, the uncertainty from test part non-homogeneity could be determined by prompting each operator to measure each test part at four different measuring points twice. This would lead to 3 · 10 · 4 · 2 = 240 measurements. The required effort is economically not feasible. For this reason, the GUM [22] states: “This is rarely possible in practice due to limited time and resources”. There are two alternatives in order to minimize this effort: Reducing the number of experiments Design of experiments provides procedures for reducing the number of experiments without any major loss of information. It is recommended to use D-optimum experimental designs in the case of multistage factors. The estimation of variance components is based on the method of moments (ANOVA see Annex A.2). The corresponding experimental design can be created by suitable computer software automatically according to specified information about the experiment. Example for a D-optimum experimental design In order to estimate the standard uncertainty from the reproducibility of operators uAV, the uncertainty from the maximum value of repeatability or resolution uEV and from test part non-homogeneity uOBJ, 3 operators and 2 repeated measurements on each of 10 parts at each of 4 measuring points are required. This leads to 240 individual measurements. If a D-optimum design with a twofold interaction is created under the same conditions, the original 240 individual measurements can be reduced to 128 measure-

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ments. This almost halves the number of experiments. The example of Annex F.2 illustrates this option. Observation of a maximum of two influence factors If the example above only evaluates the influence of operators and equipment, the number of measurements is reduced. Alternatively, it is possible to evaluate two other influence factors (e.g. influence of test part and measuring instrument). Any other influence factor that is still missing is determined according to the Type A or Type B evaluation described above. Some variations might be included in several calculations. However, it is important not to consider them more than once in the evaluation of the measurement process. If, for example, the standard uncertainty uGV should be evaluated because of different measuring systems (e.g. micrometer), 1 operator can take 2 repeated measurements on each of 10 test parts from 3 identical measuring systems (1 · 10 · 2 · 3 = 60 measurements). In order to minimize the influence of the test parts, both repeated measurements should always be taken at the same measuring point. Thus, it is important to mark the measuring point used in the first measurement. 4.4.3

Type B Evaluation

If the standard uncertainty cannot be determined by the Type A evaluation or if this method is economically not feasible, the respective standard uncertainties are estimated based on available information. The pool of information may include: • previous measurement data • experience with or general knowledge of the behaviour and properties of relevant materials and instruments (similar or identical instruments) • manufacturer's specifications • data provided in calibration and other certificates • uncertainties assigned to reference data taken from handbooks • measured quantity values based on less than n = 10 measurements

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4.4.3.1

Type B Evaluation: Expanded Measurement Uncertainty UMP Known

If the available information provides numerical values for the expanded measurement uncertainty UMP and the used coverage factor k, e.g. from calibration certificates, the coverage factor k must be calculated as follows before multiplying it by the combined standard uncertainty u(y), see Chapter 4.6.

u ( xi ) =

4.4.3.2

UMP k

Type B Evaluation: Expanded Measurement Uncertainty UMP Unknown

If the expanded measurement uncertainty is unknown, a variation limit a or another upper or lower limit can be selected. The standard uncertainty u(xi) is calculated in consideration of the respective distribution type by transforming the limits of error. Table 3 contains typical distributions. Without any information about the distribution, the rectangular distribution is the safest alternative.

u( xi ) = a ⋅ b

where

a b

variation limit distribution factor

According to the International vocabulary of metrology [21], the maximum permissible measurement error is the maximum value of a measurement error relating to a known reference value. This reference value must be given in the specifications or regulations for a measurement, measuring instrument or a measuring system. The distribution factor depends on the respective distribution type (see Table 3). In estimating the standard uncertainty of the resolution of the measuring system, the rectangular distribution applies. If the range R is used as an estimator of the variation resulting from several repeated measurements (e.g. taken from a measurement standard), the distribution factor of the normal distribution (b = 0,5) is applied.

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Distribution type

Function (P = probability that the values lie within the interval ± a)

Distribution factor b

Standard uncertainty u(x)

0,5

u( xi ) = 0,5 ⋅ a

Normal distribution (Gaussian distribution)

-a

0

+a

(P = 95,45 %)

1

Rectangular distribution -a

0

u( xi ) =

3

+a

a

3

(P = 100 %)

Table 3:

4.5

Typical distribution types and associated variation limits for determining the standard uncertainty by the Type B evaluation

Combined Standard Uncertainty

In accordance with the mathematical model, the combined standard uncertainty u(y) is calculated from all standard uncertainty components obtained in the Type A and Type B evaluation. However, in the special cases described in Chapter 4.4 where the sensitivity coefficients equal “one”, the combined measurement uncertainty is calculated using quadratic addition:

u( y ) =

n

∑ u (x ) i =1

4.6

2

i

= u ( x1 ) + u ( x2 ) + u ( x3 ) + ... 2

2

2

Expanded Measurement Uncertainty

A measure of uncertainty with which the true value may vary from the measured value is termed expanded measurement uncertainty UMP. It is calculated by multiplying the combined measurement uncertainty by the coverage factor k (see Table 4):

UMP = k ⋅ u( y )

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The expanded measurement uncertainty UMP is calculated from a two-sided, limited probability density function of the combined measurement uncertainty based on a level of confidence of P = 1 − α = 0,9545 with an interval of α / 2 beyond the distribution quantiles. The special case of a symmetric distribution leads to the following calculation formula of the expanded measurement uncertainty: UMP = k ⋅ u(y ) and by assuming a normal distribution k = z1−α 2 = 2 . Assuming a normal distribution, the values and intervals of Table 4 apply. Coverage factor 1 2 3 Table 4:

Level of confidence 68,27% 95,45% 99,73%

Coverage factors

If the probability density function does not follow a normal distribution (e.g. in case of an asymmetric distribution), high levels of confidence, in particular, can lead to sharp deviations from the values listed above (see Annex D). Remark:

The level of confidence of 95,45% and the coverage factor k=2 is recommended for calculating the capability of measuring systems and measurement processes.

These assumptions allow for a statement about the probability that the true quantity value of the measurand yi lies within the interval. measurement result Y

y i - UMP ,..., y i + UMP

UM P

U MP

measurement value yi

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4.7

Calculation of Capability Ratios

When inspecting by variables (measuring), the capability of a measurement process is established by determining the expanded measurement uncertainty specific to the respective measurement task in consideration of each dominant influence factor (see Chapter 4.1). The characteristics and specifications to be tested must be determined before the inspection starts. Figure 7 shows a flow chart for assessing the capability of measuring systems or measurement processes. In case of inspections by attributes (gauging), special analyses are required in order to establish the capability of measurement processes (see Chapter 9).

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The capability ratios QMS for the measuring system and QMP for the measurement process help to evaluate metrological demands on the measuring system or measurement process. They are defined as capability ratios and expressed as percentages.

QMS =

2 ⋅ UMS TOL

⋅ 100%

or

QMP =

2 ⋅ UMP TOL

⋅ 100%

The capability ratios are associated with the respective limits QMS_max or QMP_max. If it is demonstrated that the capability ratios QMS < QMS_max

or

QMP < QMP_max,

do not exceed these limits, the capability of the measuring system or measurement process is established.

Remark:

According to ISO/TS 14253 [13], the tolerance zone is reduced on either side by the expanded measurement uncertainty UMP. For this reason, the ratio of 2·UMP is used as the tolerance TOL for the capability ratio.

-UMP +UMP

-UMP +UMP

2·UM P

L Figure 8:

U

Illustration of a capability ratio

The limits for the capability of measuring systems and measurements processes must be determined. It is important to consider that the influences of the form deviation of test parts can affect the evaluation of the measurement process considerably. It is recommended that the capability ratio for measuring systems, QMS_max amounts to 15% and, for measurement processes, QMP_max amounts to 30%.

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Remark 1:

The proposed limits serve as guide values that cannot be generalized in any case. In individual cases, the limits must be agreed upon between supplier and customer. If the proposed limits are unrealistic, individual agreements must be made depending on the respective characteristic and its specifications (wide or narrow/very narrow tolerances). It is important always to take into account the entire measurement process. In order to determine the limits, it is necessary to consider the economic and technical requirements. For this reasons, the limits should be as wide as possible and as narrow as necessary.

Remark 2:

If the capability of the production process reaches a sufficiently high value (e.g. Cp, Cpk ≥ 2,0) that was established by an adequate measurement process, a separate observation of the expanded measurement uncertainty at the specification limits is not required because the evaluation of the process already includes the variation of the measurement process.

The capability ratio QMP corresponds to the percentage by which the tolerance zone of the test characteristic is reduced or extended according to ISO/TS 14253 Part 1 [13]. Chapter 4.10 illustrates the relation between the observed capability index and the real capability index in case of a twosided tolerance zone for various QMP values. As shown in Figure 9 and Table 6, the effects can be significant. Remark:

Determination of the uncertainty components of the measuring system is not required when the MPE has been proved and documented:

uMS = MPE

3

If more than one MPE value affects the combined standard uncertainty of the measuring system. the following formula applies:

u

2 MS

MPE12 MPE 2 2 = + + ... 3 3

uMS =

MPE12 MPE22 + + ... 3 3

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4.8

Minimum Possible Tolerance for Measuring Systems / Measurement Processes

In order to classify measuring systems and measurement processes, it is advisable to calculate the minimum tolerance required to establish the capability of the measuring system and the measurement process. This tolerance is calculated by rearranging the formula and replacing QMS or QMP by QMS_max or QMP_max. The result will be the minimum possible tolerance for the measuring system TOLMIN-UMS or the measurement process TOLMIN-UMP:

TOLMIN -UMS =

2 ⋅ UMS ⋅ 100% QMS _ max

TOLMIN -UMP =

2 ⋅ UMP ⋅ 100% QMP _ max

or

The inspected measurement process can be used down to the minimum tolerance value of TOLMIN-UMP. Remark 1:

If the minimum tolerance value TOLMIN-UMS for the measuring system is already similar to the specified tolerance TOL, an estimation of the standard uncertainties of the measurement process is unnecessary because the result would exceed the QMP_max value anyway, unless the uncertainties are negligibly small.

Remark 2:

This procedure is very useful in case of standard measuring instruments and similar measurement tasks.

Remark 3:

The calculated minimum tolerance only applies to the respective measurement task.

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4.9

Uncertainty Budget

An uncertainty budget gives a clear overview of the capability of measuring systems and measurement processes. Table 5 shows an example of a possible uncertainty budget. Evaluation type

u(xi)

A/B

name u(xi)

A

...

...

...

Variation limit a

Coverage factor b

name u(xi)

B

u ( xi ) = a ⋅ b

Type B evaluation

Standard deviation or Ui from ANOVA

Uncertainty component (value)

Uncertainty component (name)

Type A evaluation

u(xi)

u ( x i ) = si or Ui from ANOVA

...

...

...

...

Combined measurement uncertainty

u( y ) =

n

∑ u( x )

2

i

i =1

Expanded measurement uncertainty

Table 5:

UMS = k ⋅ u( y ) UMP = k ⋅ u( y )

Information provided by an uncertainty budget

Every measured quantity value obtained in a measurement in practice includes the expanded measurement uncertainty UMP.

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4.10

Capability of the Measurement and Production Processes

Figure 9 displays the relation between observed process capability index (Cp;obs), the real process capability index (Cp;real) and the capability ratio (QMP).

Q MP

50%

40%

30%

4,00 3,80

real C value

3,60 3,40 3,20 3,00 2,80 2,60 2,40 2,20

1,72

20% 10%

2,00 1,80 1,60 1,40 1,20 1,00 0,80 0,60 0,40

0,20 0,00 0,50 0,60 0,70 0,80 0,90 1,00 1,10 1,20 1,30 1,40 1,50 1,60 1,70 1,80 1,90 2,00 1,33 1,67

observed C value

Figure 9:

Display of the real C-value as a function of the observed C-value subject to QMP

The curve shape displayed in Figure 9 shows that a real capability index of 2,21 from an actual production process and a measurement capability figure QMP = 40% only results in an observed capability index of 1,33. A capability ratio QMP of 10% shows to a considerably better result. In this case, an observed C-value of 1,67 corresponds to a real C-value of 1,72.

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The calculation is based on the following assumptions: •

Measured quantity values of the manufactured characteristic are normally distributed.

•

The calculation of the Cp index is based on 99,73% reference value estimated by 6 standard deviations.

•

The observed, empirical standard deviation is: sobs =

•

The uncertainty range regarding the specification limits is symmetrical.

2 2 sreal + sMP

• The coverage factor used to calculate the combined uncertainty is 2. Based on the curve shapes (Figure 9), the Cp;real and Cp;obj values can be specified for typical C-values as a function of QMS (Table 6). Real C-value for the process when… Observed Cvalue

QMP = 10%

QMP = 20%

QMP = 30%

QMP = 40%

QMP = 50%

0,67

0,67

0,68

0,70

0,73

0,77

1,00

1,01

1,05

1,12

1,25

1,51

1,33

1,36

1,45

1,66

2,21

18,82

1,67

1,72

1,93

2,53

2,00

2,10

2,50

4,59

Table 6:

4.11

Relation between Cp;real and Cp;obs for typical C-values

Dealing with Not Capable Measuring Systems / Measurement Processes

In order to improve a measuring system / measurement process, the standard uncertainties must be reduced, for example, •

by using measurement procedures including a lower measurement uncertainty and

•

by reducing the effects of the influence factors affecting the measurement process (see Figure 5).

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The application of measurement procedures resulting in a lower measurement uncertainty is a simple solution, however, they must be proved economically optimal for performing the measuring task. Here are some examples of how to reduce the effects of influence factors on the measurement uncertainty: Measuring equipment / material measure • selecting more suitable sensors • selecting material measures of a higher quality • selecting a sampling strategy • optimizing the sampling strategy (e.g. measuring speed, definition of measuring points, mounting device, settings, algorithms for evaluation, sequence) • repeated measurements including averaging (Annex C) Test parts • correcting temperature of a test part to a standard temperature of 20° C • cleanliness • improving dimensional stability and surface properties • avoiding burrs Operator • improving skills and qualifications of operators • taking measures to raise employee motivation Environment (temperature, vibrations, etc.) • avoiding negative influences by selecting proper workstation or screen • taking measurements under temperature-controlled conditions • positioning measuring instruments in a place where they are protected against vibrations Stability of a measuring instrument (stability) • detecting and correcting components causing a trend

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5

Measurement Process Capability Analysis

5.1

Basic Principles

The previous chapters dealt with the following, general topics: • necessity to determine the expanded measurement uncertainty UMS for a measuring system and UMP for a measurement process • calculation of the expanded measurement uncertainties UMS and UMP based on the combined measurement uncertainty uMS or uMP and the coverage factor k • criteria for the capability ratios of measuring systems QMS and measurement processes QMP • schematic approach for proving the capability of a measuring system and measurement process This chapter explains how to determine the individual uncertainty components u(xi) either by using the Type B evaluation (see Chapter 4.4) or by experiment (see Type A evaluation, Chapter 4.4). For this purpose, a standardized method is available and recommended covering a large part of measurement uncertainty estimations that occur in practice. In some cases, where the preconditions set out for this method are not present, the user must use the general current method for determining the measurement uncertainty that is described in the “Guide to the expression of uncertainty in measurement“ (ISO/IEC Guide 98-3 [22]). If the uncertainty components estimated from an experiment do not correspond to the expected spread of these components in the actual measurement process, then these components must not be estimated experimentally. Instead, they should be derived using a mathematical model (e.g. constant temperature in a measuring laboratory when conducting a test and the normal temperature variations of the place of the future application). In this model, the expected variation in the real measurement process must be considered. The following chapters, however, are based on the assumption that only the uncertainty components test part homogeneity, resolution and temperature should be derived using a mathematical model.

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5.2

Capability Analysis of a Measuring System

In principle, the expanded measurement uncertainty refers to the entire measurement process (see Chapter 4.6). However, since the measuring system is an essential part of the measurement process, it should be evaluated separately. Its capability ratio QMS (see Chapter 5.2.1) is generally easier to determine than the capability of the measurement process. Measuring systems require that the resolution (%RE) should be lower than 5% of the specification. If this requirement is not satisfied, a different measuring system has to be applied. Uncertainty components related to the measuring system are “calibration uncertainty on the reference standard“, “uncertainty from bias,”, “uncertainty from measurement repeatability” and “uncertainty from linearity” (see Table 7). The standard uncertainty due to the calibration on the reference standard is given in the calibration certificate. If the bias is not compensated by calculation, repeated measurements are taken on one, two or three measurement standards, depending on the measuring system and measurement task. The values of the standards are approximately equidistantly placed throughout the relevant measuring interval associated with the measurement process (see Figure 14). The measured quantity values form the basis of determining the standard uncertainties due to the bias and equipment influences. Before starting the analysis, the working point(s) of the measuring system must be set accordingly. For further information, see Annex E. If the bias of the measuring system can be corrected, the regression function has to be determined by ANOVA (see Chapter 5.2.2). In this case, repeated measurements are taken on at least three measurement standards whose values are placed throughout the relevant measuring interval (see Figure 14). These measured values are used to calculate the regression function and the compensation is made. In spite of the compensation, some uncertainties are remaining. They are composed of the pure error standard deviation uEV and the lack-of-fit uLIN. Both must be considered in calculating the combined standard uncertainty of the measuring system. Figure 10 shows a flow chart of the measuring system capability analysis. Table 7 explains how to determine single standard uncertainties. Chapter 4.7 describes how to calculate the capability ratio QMS or the minimum permissible tolerance TOLMIN-UMS.

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Measurement System Capability

%RE

no

5%TOL

Use measurement system with a sufficiently high resolution

yes yes

MPE known and accepted? no

yes

no Linearity uLIN known?

Prepare trial

Prepare trial

minimum 3 reference standards, repeat measurements

1, 2, or 3 reference standards, repeat measurements

UCALi uCAL = max 2

UCALi uCAL = max 2

document MPE

{ }

from ANOVA:

uEVR = max sgi

uEVR (pure error deviation)

Bi uBi = max i 3

from ANOVA: uLin (lack-of-fit deviation)

uMS see table 12

UMS = k ⋅ uMS

Measurement system capable

QMS =

yes

2 ⋅ UMS ⋅ 100% TOL

QMS

QMS_max

TOLMIN -UMS =

no

2 ⋅ UMS ⋅ 100% QMS _ max

Measurement system not capable

Figure 10: Measuring system capability analysis

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Uncertainty components

Symbol

Test / model %RE must be lower/equal than 5% of the specification

Resolution of the measuring system

uRE

Calibration uncertainty

uCAL

Repeatability on reference standard

uEVR

Uncertainty from bias

uBI

1 RE 1 ⋅ = ⋅ RE where RE is the resolution 3 2 12 See note on page 56. Obtained from the calibration certificate of measurement standards. In cases where the uncertainty in protocol is given as an expanded uncertainty, it should be divided by the corresponding coverage factor: uCAL = UCAL / kCAL Depending on the measuring system, repeated measurements are taken on one, two or three standards. On one measurement standard, at least 25 repeated measurements are taken whereby their spread uEVR =sg can be estimated. On each of two standards, at least 15 repeated measurements are taken whereby their spread uEVR can be estimated. The greatest one of the results is used. On each of three standards, at least 10 repeated measurements are taken whereby their spread uEVR can be estimated. The greatest one of the results is used. From the measured values on a reference standard taken during a repeatability analysis, the standard uncertainty uBI can be calculated based on the systematic measurement error from: uRE =

uBI =

xg - xm 3

In case of two or three measurement standards, the greatest one of the results is used.

Uncertainty from linearity

uLIN

Uncertainty from other inuMS_REST fluence components Table 7:

52

In the calculation of linearity, uLIN is determined by the method of ANOVA (lack-of-fit deviation / see Annex A.2). For measuring systems with linear material measure, the uncertainty from linearity is determined based on the results from the manufacturer’s or calibration certificate. Any further influences on the measuring system, supposed or substantial, must be estimated separately by experiments or from tables and manufacturer’s specifications.

Typical uncertainty components of a measuring system

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Remark:

The ISO/TS 15530 [16] adds the bias BI as a whole to the other components in order to calculate the combined standard uncertainty for the measuring system uMS:

uMS =

(u

2 CAL

2 + uEVR ) + Bi

It is assumed that Bi is generally small. If the bias is large, it must be corrected on the measuring system. In order not to make a general decision, this document treats the standard uncertainty arising from the bias as any other standard uncertainty component:

uMS =

(u

2 CAL

2 + uEVR + uBI2 )

In order to make the two formulas comparable, only the uCAL, uEVR and uBi components were observed.

The estimation of each single uncertainty component is not required when the maximum permissible error MPE of the measuring system is known, traceable and documented. In this case, uMS is determined by MPE ( uMS = MPE 3 ). However, calculations referring to characteristics require these estimations. The following chapters explain how to determine the respective standard uncertainty. 5.2.1

Resolution of the Measuring System

In order to establish the capability of a measuring system, its resolution (see Table 7) must not exceed 5 % of the specification. For this reason, the

standard uncertainty arising from the resolution is only considered for measurement processes. RE is the smallest step (between two scale marks) of an analogue measuring instrument that can be read clearly or the step in last digit of a digital display (e.g. 0,001, 0,5 or 1,0). 5.2.2

Repeatability, Systematic Measurement Error, Linearity

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In order to determine the uncertainty arising from the repeatability on a measurement standard, it is recommended to use the experiment known as a “Type 1 study”, used for determining the measuring system capability indices Cg or Cgk (see Chapter 5.2.2.1 and [25]). This study can also be applied to two or three standards. If the linearity of the measuring system has to be determined, it can be done by means of a linearity study based on at least three reference standards. The result of this investigation (regression function) can then be used for correction of the measurement result which reduces the uncertainty from linearity.(see Chapter 5.2.2.2).

5.2.2.1

Estimating the Systematic Measurement Error and Repeatability according to the “Type 1 Study”

The systematic measurement error (bias) must be reduced as far as possible by adjustment or calculation. Nevertheless, some small or unknown residual systematic errors will remain. The errors are the maximum values of the known systematic measurement errors within the used measuring interval and cannot be corrected. This error can be estimated by an investigation on a measurement standard (material measure). This study can also be applied with several standards. Repeated measurements on a standard In order to determine the uncertainty from repeatability and resolution on a reference standard uEVR, it is recommended to use the experiment known as a “Type 1 study” (determining the capability of the measuring system) (see guide to the proof of measuring system capability [25]). However, in this case, the aim of the experiment is the estimation of uncertainty components rather than the estimation of the capability ratio. The determination of the uncertainty uEVR comes from the standard deviation of the repeatability sg estimated from measurements on a measurement standard. It should be based on the spread of a minimum of 25 repeated measurements, to estimate the combined effect of bias and repeatability.

uEVR = sg =

54

K 1 ⋅ ∑ y i - xg K - 1 i =1

(

)

2

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where:

K number of repeated measurements, normally K = 25 or more yi single value of the i-th measurement x g the arithmetic mean of all the sample values

The standard uncertainty uBI from bias is calculated from:

uBI = where:

xm

xg - x m 3

reference quantity value of the measurement standard within the tolerance of the test characteristic and bias Bi

Bi = xg - xm The capability indices Cg and Cgk used in [26] are calculated from the series of measurements determined thereby:

Cg =

0,2 ⋅ TOL 4 ⋅ sg

Cgk =

0,1⋅ TOL - Bi 2 ⋅ sg

If uCAL and uBI are neglected, QMS can be compared to Cg. In this case, a Cg-value of 1,33 corresponds to a QMS_max -value of 15 % (see Chapter 4.7). Remark:

There are several company guidelines using a sample standard deviation of 6sg or 3sg (coverage probability P = 99,73%) instead of 4sg of 2sg (P = 95,45%). In this case, a Cg-value of 1,33 corresponds to a QMS_max-value of 10% (see Chapter 4.7).

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The comparison between the presented determination of standard uncertainties and calculation of capability indices shows that the procedure, in order to obtain measured quantity values, is the same. The difference lies in the derived statistical values: •

uEVR and uBI

•

Cg

(measurement uncertainty)

and Cgk (capability of measuring system)

and in the interpretation of results. In this way, available measured quantity values gained in previous capability analyses according to the „Type 1 study” for determining the standard uncertainties can be used. Remark:

The result of uEVR can be compared to uRE. The greater value of the two is used as the standard uncertainty from repeatability uEV. Reason: Even though the requirement %RE ≤ 5% is satisfied, it is possible that, for example in case of 25 repeated measurements on a reference standard, the variation may be zero (uEVR =0) or only one value differs in its resolution from the other values of a series of measurements. In this case, it generally applies uEVR < uRE. Example: A diameter of 20 ± 0,2 mm is to be inspected. A digital micrometer with a resolution of 0,01 mm (%RE = 2,5 %) meets the requirement %RE ≤ 5%. If this micrometer performs 25 repeated measurements on a gauge block (20 mm), a value of 20,00 is frequently obtained. This leads to an uncertainty uEVR amounting to zero. In this case, the standard uncertainty from the resolution of the measuring system uRE = 2,89 µm must be used rather than the standard uncertainty from repeatability.

Example on one measurement standard In this example, a characteristic with a nominal quantity value of 6 mm is used. The upper specification limit is U = 6,03 mm and the lower specification limit is L = 5,97 mm. This leads to a specification of 0,06 mm. The uncertainty from linearity is negligibly small (uLIN = 0). The resolution of the used measuring system amounts to 0,001 mm (≙%RE = 1,66%). Thus, the requirement %RE ≤ 5% is fulfilled. The calibration certificate for the reference standard with a reference quantity value of 6,002 mm gives UCAL= 0,002 mm and kCAL= 2. In this example, 50 repeated measurements (25 would be sufficient) are performed on the reference standard (see Table 8).

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Table 8:

Measured values of the repeated measurements on the standard

From these data and measured quantity values, the following standard uncertainties and results of the measuring system are obtained:

Figure 11: Standard uncertainties of the measuring system

Figure 12: Results of the measuring system The measuring system is applicable down to a minimum tolerance of 0,042 mm.

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Remark:

The results are based on a statistical evaluation whose informational value must be assessed by means of the confidence interval. However, this is not done in this example. Thus, a repetition of the experiment or different sample sizes leads to slightly different results.

Repeated measurements on two measurement standards For this analysis, the use of a material measure is recommended whose actual values lie within a range of ± 10% around the specification limits (see Figure 13). Before starting the study, the measuring system must be set according to the procedure described in Annex E.

-10%

+10%

-10%

xml

+10%

xmu

L

U

lower tolerance limit

upper tolerance limit

Figure 13: Recommended location of the material measure xml actual value of material measure near the lower specification limit L xmu actual value of material measure near the upper specification limit U In general, a minimum of 15 repeated measurements should be performed on each measurement standard. Based on these measurement results, uEVR and uBI are estimated for each measurement standard according to the described procedure associated with standards. The greater value of the two serves as the uncertainty component uEVR or uBI.

UEVR = max UBI = max

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Repeated measurements on three measurement standards For this simplified linearity analysis, the use of a material measure is recommended whose actual values lie within a range of ± 10% around the specification limits (Figure 14).

-10%

+10%

xml

-10%

+10%

-10%

xmm

L

+10%

xmu U

Figure 14: Recommended location of the material measure

xml actual value of material measure near the lower specification limit L xmm actual value of material measure near the center of the specification xmu actual value of material measure near the upper specification limit U In general, a minimum of 10 repeated measurements should be performed on each measurement standard. Based on these measurement results, uEVR and uBI are estimated for each measurement standard according to the described procedure associated with standards. The greater value serves as the uncertainty component uEVR or uBI.

UEVR = max. {uEVR1, uEVR2, uEVR3} UBI = max. {uBI1, uBI2, uBI3} In this case, the standard uncertainty from linearity is part of UBI. This leads to ULIN = 0.

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5.2.2.2

Linearity Analysis with Correction on the Measuring Instrument

The following procedure is suggested: • On each of a minimum of three reference standards at least 10 repeated measurements are performed (the number of standards multiplied by the number of repeated measurements must lead to a minimal sample size of 30). • The reference standards should be evenly spread over the entire specification zone. The areas associated with the specification limits displayed in Figure 13 must be considered. • A regression analysis is performed in order to estimate the linear regression function by assuming that the pure error standard deviation is constant over the spread of measurement results (see Figure 15 and Annex A.1). • An analysis of variance is performed whereby residuals are analyzed due to a lack-of-fit and pure error standard deviation (see Figure 15 and Annex A.2). • Estimation of the uncertainty components based on the results of the method of ANOVA. • Correction on the measuring system, i.e. correction on future measurements (where appropriate). Generally, the following preconditions apply: • The pure error standard deviation (standard deviations from repeated measurements on the standards) is always constant. • The regression function is linear (regression line). • The calibration uncertainty on the reference standards is lower than 5 % of the specification. • The measurements are representative of the future use of the measuring system regarding the environment and other conditions. • The repeated measurements of the reference standards are independent from each other and the results are normally distributed. • The values of the standards are approximately equidistantly placed throughout the relevant measuring interval.

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Example of a linearity analysis with regression analysis For a better illustration of this issue, the example includes a high lack-of-fit and a considerable pure error standard deviation. This leads to great uncertainties in the end. Moreover, more than three reference standards are used. This is quite unusual in practice. In a linearity analysis, 5 repeated measurements (K=5) on each of 6 reference standards (N=6) are performed. The minimum requirement of a sample size of N ⋅ K =30 is satisfied. The following values (in mm) were determined:

Table 9:

Measured quantity values of the analysis

Assuming that the preconditions listed in Chapter 5.2.2.2 are fulfilled, the regression function is calculated from the reference quantity values xn and the measured quantity values ynk. Annex A.1 contains the formulas for estimating the unknown parameters of the function. regression function:

yˆ = -0,6176 + 0,9183 ⋅ x

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Figure 15: Diagram of an analysis of variance Figure 15 displays relevant components of the regression function and the analysis of variance and their relation to one another. The diagram gives an initial impression regarding the following information: • whether the measurement process is under statistical control during the experiment • correctness of assuming a constant linearity (lack-of-fit) • deviation of the measured quantity values from the regression line (residuals) • deviation of the single repeated measurements on a reference standard (pure error standard deviation) • the presence of outliers that need further investigation

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For an evaluation, the residuals enk can be observed in a value chart (see Figure 16 a)). In order to find out whether the single measurements are independent from one another, the residuals enk must be normally distributed. This can be seen by b) plotting them on probability plot (see Figure 16). Here, the measured quantity values should adapt to the probability straight line as far as possible. The spread of the residuals enk can be obtained by c) plotting them on the fitted values yˆn (see Figure 16).

a.)

b.)

c.)

Figure 16: a.) Value chart of the residuals b.) Residuals plotted on a probability plot c.) Residuals plotted on fitted values

If there are inconsistencies in the graphical display, they must be eliminated. If necessary, the analysis must be repeated. After the graphical evaluation of the regression function and the residuals, the estimates of the uncertainty components uLIN and uEVR should be calculated by using the method of ANOVA. Annex A.2 provides the required ANOVA table with the associated formulas.

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A given calibration uncertainty of uCAL = 0,05, a resolution of RE = 0,001 mm and a tolerance of TOL = 30 mm lead to the following results:

Figure 17: Uncertainty budget of the measuring system

Figure 18: Result for the measuring system Due to the sharp linearity deviation and repeatability, the measuring system is not qualified for the measurement task. A qualified measuring system requires a minimum tolerance of 251 mm.

5.3

Measurement Process Capability Analysis

In addition to the uncertainty components of the measuring systems described above, further uncertainty components must be determined in order to evaluate the measurement process under real conditions. The procedure displayed in Figure 19 is recommended in order to perform a measurement process capability analysis.

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Table 10 and 11 contain the single standard uncertainties and Table 14 explains how to estimate or calculate the respective standard uncertainty. Table 12 gives an overview of how to calculate the expanded measurement uncertainty of the measuring system UMS and the measurement process UMP. It also contains the capability ratios for the measuring system QMS and the measurement process QMP. By comparing these results to a specified limit, it is possible to determine whether the respective measuring system or measurement process is qualified for the intended measurement task. If the ratio exceeds or goes below the specified limit, the following questions can be answered by rearranging the stated equation. •

Statistic exceeds limit: “What is the minimum tolerance demanded in order, just barely, to achieve capability? “

•

Statistic goes below limit: “What is the maximum tolerance demanded in order, just barely, to achieve capability? “

This requires the calculation of the statistics for the measuring system TOLMIN-UMS and the measurement process TOLMIN-UMP.

Uncertainty components Repeatability on test parts Reproducibility of operators Reproducibility of measuring systems (place of measurement) Reproducibility over time Uncertainty from interaction(s)

Table 10:

Symbol uEVO uAV

uGV uSTAB uIAi

Test / model Minimum sample size: 30 Always a minimum of 2 repeated measurements on a minimum of 3 test parts measured by a minimum of 2 operators (if relevant), measured by a minimum of 2 different measuring systems (if relevant) see “Type 2 study” MSA [1] Estimation of uncertainty components by the method of ANOVA.

Typical uncertainty components of the measurement process determined in experiments (Type A evaluation)

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{

}

2 2 2 2 2 uMP = uCAL + max uEVO , uEVR , uRE + uBI2 + uLI2 N 2 2 2 2 2 2 +u AV + uGV + uSTAB + uT2 + uOBJ + ∑ uIAi + uRES T

UMP = k ⋅ uMP

QMP =

2 ⋅ UMP ⋅ 100% TOL

TOLMIN -UMP =

2 ⋅ UMP ⋅ 100% QMP _ max

Figure 19: Measurement process capability analysis

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Uncertainty Symbol components Uncertainty caused by test part nonuOBJ homogeneity

Model

uOBJ =

aOBJ

where aOBJ is the maximum form deviation (see Table 14)

3

The influence caused by temperature can be calculated using the formula: 2 2 uT = uTD + uTA

uTD uTA

where

uncertainty caused by temperature differences uncertainty caused by expansion coefficients

The uncertainty caused by temperature differences could e.g. be estimated in compliance with ISO/TR 14253 Part 2 [15]:

uTD = ∆T ⋅ α ⋅ l ⋅ α ∆T l

uT

1

where

3

expansion coefficient temperature difference observed value for length measurement

If a measuring instrument is set using one reference part and the test part and reference part have different temperatures and expansion coefficients, uTD can be calculated from the difference ∆l of the expansion between test part and the working standard:

Uncertainty caused by temperature

uTD = ∆l ⋅

1 3

The uncertainty on expansion coefficients could e.g. be estimated in compliance with ISO/TR 15530-3 [16]:

uTA = T - 20 °C ⋅ uα ⋅ l where average temperature during the measurement uncertainty on the coefficient of expansion l observed value for length measurement alternatively: see Annex C1, uncertainty with correction of the different linear expansions see Annex C2, uncertainty without correction of the different linear expansions Any further influences of the measurement process must be estimated separately. T

uα

Uncertainty caused by other influence comuREST ponents

Table 11:

Typical uncertainty components of the measurement process from available information (Type B evaluation)

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Table 12 gives an overview of the calculation of the combined measurement uncertainty, the expanded measurement uncertainty and the capability ratios or the minimum tolerance of the measuring system and the measurement process. Uncertainty components

Symbol

Calibration uncertainty on standard

uCAL

Uncertainty from bias

uBI

Uncertainty from linearity

uLIN

Repeatability on standards

uEVR

Uncertainty from other influence components (measuring system)

uMS_REST

Combined measurement uncertainties

Expanded measurement uncertainties

u MS = 2 uCAL

{

+u + u 2 BI

2 LIN

+u

QMS =

}

2 2 + max u EVR , uRE

2 MS _ REST

U MS = k ⋅ uMS

or

MPE

2

TOL

⋅ 100%

TMIN −UMS =

or

Maximum permissible error

MPE

2 1

MPE 3

Repeatability on test part Reproducibility of operators Reproducibility of measuring systems Reproducibility over time Uncertainty from interaction(s) Uncertainty from test part inhomogeneity Resolution of the measuring system Uncertainty from temperature Uncertainty from other influence components

68

2 ⋅ UMS

2 ⋅ U MS ⋅ 100% QMS_max

3

Table 12:

Capability ratio minimum tolerance

+

MPE22 … 3

uEVO uAV

uMP =

uGV

QMP =

uSTAB

u

uIAi

+ max u

uOBJ uRE uT

2 CAL

{

2 EVR

2 EVO

, u

2 RE

, u

}

2 +uBI2 + uLIN

U MP = k ⋅ u MP

2 2 2 2 +uAV + uGV + uSTAB + uOBJ 2 +uT2 + uREST + ∑ uIA2

2 ⋅ U MP TOL

⋅ 100%

TMIN −UMP = 2 ⋅ UMP ⋅100% QMP_max

i

i

uREST

Calculation of the expanded measurement uncertainty of the measuring system / measurement process and their capability

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5.3.1

Example for Determining the Uncertainty Components of the Measurement Process

In order to determine the capability of a measurement process, the standard uncertainties of the measuring system were estimated (see example with one standard in Chapter 5.2.2.1) and an experiment was conducted by 3 operators performing 2 repeated measurements on each of 10 test parts. The results were evaluated by means of the method of ANOVA (see MSA [1]). Table 13 lists the measured quantity values leading to the standard uncertainties shown in Figure 20 and the results displayed in Figure 21. Since the interactions between operator and part is not significant, pooling is used in the calculation according to the method of ANOVA (see Annex A.2).

Table 13:

Remark:

Measured quantity values taken in 2 repeated measurements on 10 parts by 3 operators

According to MSA [1], the statistical value %GR & R EV 2 AV 2 is calculated from the measured quantity values by using the same calculation method of ANOVA. In this case EV=uEVO and AV=uAV. This example again shows the similarities between MSA and VDA 5. The difference does not lie in the procedure, but in the different statistics and interpretations.

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Figure 20: Standard uncertainties of the measurement process

Figure 21: Results of the measurement process The measurement process is applicable down to a minimum tolerance of 0,03 mm (rounded figure).

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6

Ongoing Review of the Measurement Process Capability

6.1

General Review of the Measurement Stability

The short-term as well as the long-term stability has to be taken into account when the capability of the measurement process is calculated. However, a change in bias caused by drift, unintentional damage or new additional uncertainty components, which were not known by the time of calculation of the capability, can change the bias in the measurement process over time so that capability is not established anymore. A control chart should be used to be able to determine those possible significant changes in the measurement process. The following sequence is recommended:

Step 1: Select an appropriate reference standard (working standard) or calibrated work piece with a known value for the test characteristic. Step 2: Carry out regular measurements on the reference standard (working standard) or test part (e.g. every day in a working week or at the beginning / end of a shift or prior to each measurement in case of a measurement process used only rarely). Step 3: Plot the measured values on a control chart. Remark:

The action limits, are calculated in accordance with known methods of quality control charting techniques.

Step 4: Case 1 If no out of control signal is detected, it is assumed that the measurement process has not changed significantly. Case 2 If an out of control signal is detected, the measurement process is assumed to have changed and shall be reviewed. With this approach, the measurement process is continuously monitored and significant changes can be detected. The resulting knowledge about the measurement process can be taken into account when determining the qualification interval for calibration.

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6.2

Correcting the Regression Function

If there was doubt about the linearity of the measuring system during the calculation and if a regression function has been experimentally determined, the method given here can be used for the ongoing review of the linearity of the measuring system. A control chart gives a signal when the regression function needs to be updated.

Step 1: Calculating control limits with figures found in Chapter 5.2.2.2 σˆ ⋅ t α ⋅ (N ⋅ K - 2) βˆ1 (1- 2⋅m )

upper control limit:

UCL =

lower control limit:

LCL = -

σˆ ⋅ t α ⋅ (N ⋅ K - 2 ) βˆ1 (1- 2⋅m )

Step 2: Selecting the m reference standards The reference standards (minimum 2) must be chosen in a way that their nominal values cover the range of observations that occur under the actual production conditions. Step 3: Repeating measurements on the reference standards For example, the reference standards should be measured every day in a working week.

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Step 4: Transforming the p measurement values on the m standards The p values of the m standards are transformed with the help of the regression function:

x=

y − β0

β1

Then each of the differences between the "true" and the transformed values is calculated. Step 5: Plotting the differences on a control chart The differences calculated in Step 4 are plotted on the time axis. Step 6: Deciding the validity of the regression function This decision will depend on whether all the differences of all standards are within the control limits.

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7

Practical Guidance to Determining Typical Standard Uncertainties

Table 14 gives notes and suggestions together with the associated references about how to determine the standard uncertainties from the respective influence factor.

Source of uncertainty Resolution of the measuring system uRE

Suggestions / remarks RE= is the smallest step (between two scale marks) of an analogue measuring instrument or the step in last digit (e.g. 0,1/0,5/1,0) of a digital display. The resolution should be much lower than the specification interval for the test part to be measured (e.g. %RE ≤ 5% of the specification interval). In this case, the resolution is included in the repeatability.

Type A/B B

Reference Reading / estimations or manufacturer’s specification

Calculate the standard uncertainty from resolution using the formula: uRE =

Calibration uncertainty on the standard uCAL

1 RE 1 ⋅ = ⋅ RE 3 2 12

In metrology, a coverage factor of k=2 is typically used in calculations. The standard uncertainty uCAL is calculated by dividing the expanded uncertainty UCAL by the coverage factor 2. The respective K-value is taken from the calibration certificate.

B

Calibration certificate / manufacturer’s specification / internal calibration

Remark The calibration uncertainty shall be much lower than the expected measurement uncertainty.

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Source of uncertainty

Suggestions / remarks

Repeatability uEVR (on standard ) and estimation of the uncertainty from bias UBI

The uncertainty components can be determined experimentally. Before using a measuring system, it must normally be set using one or two standards. The deviations from the reference quantity value determined by calibration must be considered. Remark Measurement on one reference standard In general, at least 25 repeated measurements on one standard are performed. The standard must be clamped, released, and always measured in the same place of measurement (when the influence of the standard shall not be considered). Determine uEVR (standard deviation of the sample). Calculate UBI (bias). If the relation between the single influence factor of the systematic measurement error is known, the measuring system can be corrected using the bias.

Repeatability (on 2 standards near upper and lower specification limit) max uEVR

Measurement on 2 reference standards Determine the specification limits and adjust measuring points : zero and amplification. 2x15 repeated measurements are generally performed. Similar to measurement on one standard but at the upper and lower specification limit. For further investigation, it is recommended to use the highest standard uncertainty of uEVR1 and uEVR2.

Type A/B

A

A

Reference

Experiment Type 1 study [25]

Experiment 2x Type 1 study [25]

B Model If the influences of the adjusting procedure are known, a specific model can be created. In case of mechanical measuring equipment for length measurements, these are influence factors such as: form deviations, geometrical deviations of the working standards, positioning accuracy of the test part, manufacturing and assembling tolerances depending on the measuring system, sampling strategy, algorithms for evaluation, calibration and setting position

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Source of uncertainty

Suggestions / notes

Uncertainty from lin- Case 1 earity uLIN Using manufacturer’s specification Where value a is specified by the manufacturer: uLIN = 1 3 ⋅ a Case 2 Measurement on 3 reference standards Always a minim of 10 repeated measurements on each of 3 reference standards. Minimum sample size of 30. Standards must be clamped, released, and always measured in the same place of measurement. Case 3 Measurement on three or more reference standards (regression function) In order to apply this method, the regression function must be considered in the calculations performed by the measurement software. The evaluation of uLIN based on this method only provides the corrected values that are not taken into account on the measuring system. Reproducibility of Always 2 repeated measurements on each of operators (operator 10 test parts by 2 or 3 operators influence) using test Special case: If less than 10 test parts are parts uAV available, a minimum of 2 repeated measurements on a minimum of 3 test parts by 2-3 operators is required. Remark The test parts used in the experiment should be evenly spread over the entire tolerance zone. Test parts must be clamped, released, and always measured in the same place of measurement. Sequence for repeated measurements: Measure test parts 1 - n and repeat these measurements. In case of the series of measurements, the single operators must not remember the results of the previous measurement.

Type A/B

Reference

B

Manufacturer’s specification

A

Experiment with three standards see Annex E

A Experiment with standards see Chapter 5

A

Experiment Type 2 study [1], [25]

Determine uAV using the method of ANOVA.

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Source of uncertainty

Suggestions / remarks

Repeatability on test Always 2 repeated measurements on each of parts without opera- 25 test parts. tor influence uEVO Application in (semi-)automated measuring systems or whenever the operator does not affect the measurement result. Remark The test parts used in the experiment should be evenly spread over the entire tolerance zone. Test parts must be clamped, released, and always measured in the same place of measurement. Sequence for repeated measurements: Measure test parts 1 - n and repeat these measurements. In case of the series of measurements, the single operators must not remember the results of the previous measurement. The result includes the mutual interaction between test part, measuring system, etc. Reproducibility of the Relevant to min. 2 measuring systems equal measuring Evaluation systems (place of The following generally applies to standards: measurement) uGV Observe the variation per place of measurement Compare the measured quantity value x to the calibrated values (bias) Observe max – min of the measured quantity values x for the single equal measuring systems

Type A/B A

A

Reference Experiment Type 3 study [25]

Experiment Type 1 and Type 3 study [25]

The following generally applies to test parts: Observe the variation per place of measurement Observe max – min of the measured quantity values x or the measured individuals xi per test part for each equal measuring system. The result includes the mutual interaction between test part, measuring system, etc. The experimentally determined uncertainty components are considered by using the analysis of variance (ANOVA). Remark Make this evaluation by using the same working standards and test parts. Clamp, release and measure in the same place of measurement the test parts of the 2 n measuring systems.

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Source of uncertainty

Suggestions / remarks

Reproducibility over Short-term analysis time uSTAB In general, a short-term analysis does not inspect the stability of the measuring device. Long-term analysis If measurement results are assumed to change over time in an initial or basic sampling, the uncertainty should be determined by means of specified series of measurements. Ongoing review of the measurement process capability (stability) For an ongoing review of critical characteristics or measurement processes. Remarks Working standards or test parts can be inspected. The values are plotted, for example, on a control chart and the monitoring of measurement process is based on action limits. In case of an action limit violation, UMP must be corrected. Form deviation / sur- There are different methods in order to deterface texture / materi- mine the standard uncertainty from form deviaal property of the test tion: part uOBJ (uncertainty information from drawings (maximum permisfrom test part inho- sible form deviation) mogeneity) control chart of series production (actual form deviation) test part inspected in experiment (actual form deviation) The test parts (min. 5) used in the experiment shall be evenly spread over the entire tolerance zone and represent the expected form deviation. Any further properties, supposed or substantial, must be estimated separately by experiments or from tables and manufacturer’s specifications. Uncertainty from In order to determine the uncertainty from temtemperature perature, consider whether a compensation for uT temperature difference is made. Independent of compensation or complex relations including unknown expansion coefficients, the actual expansion properties should be determined experimentally. Heat the reference standards and test parts and inspect them while they are cooling. The difference a between max and min value is used in order to estimate uT.

78

Type A/B A

Reference Experiments Type 1 study and Type 2 or Type 3 study [25]

(see Chapter 6.2)

B B

Drawing control chart

A

Experiment

Table book material data sheet

A/B

Experiment See Annex B

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Uncertainty from Any further influences, supposed or substanother influence com- tial, must be estimated separately by experiponents uREST ments or from tables and manufacturer’s specifications.

Table 14:

7.1

A/B

Experiment various documents

Methods recommended in order to determine uncertainty components

Overview of Typical Measurement Process Models

Many measurement processes are only affected by some or very few uncertainty components. For this reason, measurement process models can be defined based on equal uncertainty components (see Table 15). This overview provides help with the following questions: • What was the calibration uncertainty used in order to determine the actual value of the reference standard? • Can the purchased measuring equipment be accepted / approved for use? • What are the uncertainty components to be considered with standard measuring systems? • Are the measuring system (measuring instrument) and measuring equipment qualified for the respective specification(s)? How much do the production parts affect the measurement result or the capability of the measurement process? • What is the maximum variation of the measured quantity value? • Which factors must be considered in proving conformance or nonconformance (measurement result within or beyond the specification)?

Remark:

Models C, D and E (see Table 15) can be applied separately or are based on one another, i.e. the estimated uncertainties of model C can be transferred to model D or model E. They do not need to be determined once again.

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12 Other influences u Rest

11 Temperature uT

attribute measurement objets uOBJ

uStab 10 Form deviation/ surfaces - material

systems (measuring points) uGV 9 Reproducibility at different points in time

with serial parts uEVO 8 Reproducibility of equal measurement

partsn uAV 7 Repeatability without operator influence

6 Reproducibility of the operaor with serial

5 Linearity with master(s) uLIN

4 Repeatability with master(s) uEVR

3 Setting uncertainty uBI or Bias

MPE

2 Calibration uncertainty uCAL or error limits

1 Display resolution uRE Model A Calibration uncertainty of the reference Model B Acceptance study of the measurement process for standard measurement systems Model C Acceptance study of measurement systems Model D1 Acceptance study of the measurement process with user influence without serial part influence (measure serial parts location oriented) Model D2 Acceptance study of the measurement process without user influence without serial part influence (serial parts fed semi / automatically) Model E1 Conformity / acceptance study of the measurement process with user influence with serial part influence Model E2 Conformity / acceptance study of the measurement process without user influence with serial part influence (serial parts fed semi / automatically)

Measurement system Measurement process

green = always considered yellow = considered, if available gray = not considered for this model Table 15:

80

Typical measurement process models and their uncertainty components

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8

Special Measurement Processes

8.1

Measurement Process with Small Tolerances

Small tolerance is not a standardized term but it expresses that the tolerance is very small compared to normal conditions. Characteristic of small tolerances is that they are very hard to create and to measure. For this reason, the usual capability indices and ratios cannot be reached in the same way as those of normal tolerances. They often require conditions that are at the limits of what is physically and technically possible. Small geometric elements A small geometric element refers to very small measurement geometries available in a measurement. Only few data points can be recorded for a safe evaluation. Examples are measurements of very short lengths, measurements of very small radiuses or angular measurements where the legs of the angles are very short. In addition, the point of origin and the end point of the respective geometric element are often not clearly defined. This makes the situation even more difficult. Due to an uneven surface texture, the element does not have an ideal shape and thus, a higher measurement error must be expected. In individual cases, limits must be determined other than those mentioned in Chapter 4.8. It is not possible to determine a limit that generally applies to small tolerances because the limits also depend on the geometry and the physical and technical conditions in terms of the respective measurement task.

Remark:

8.2

Classification

In production processes including a high production variation, critical characteristics are often classified by dividing the tolerances of the relevant characteristics into two or more classes. Typical fields of application are: • • •

cylinder and piston cylinder and piston pin engine block and crankshaft

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The classification includes a 100% inspection of the relevant characteristics, the allocation of the parts to the respective class and a corresponding identification. The measurement uncertainty leads to different classifications, e.g. between manufacturer and customer, for results near the class limits obtained in repeated measurements. In order to ensure that the same parts can be assigned to a maximum of two adjacent classes in repeated measurements, the expanded measurement uncertainty is permitted to amount to a maximum of half the class width (KB): UMP / KB ≤ 0,5 In general: The maximum number of adjacent classes one part can be assigned to 2⋅UMP / KB +1 = maximum number of adjacent classes. class width KB

UMP UMP

UMP UMP

UMP UMP

tolerance

L lower specification limit

U upper specification limit

Figure 22: Classification model

8.3

Validation of Measurement Software

Current measuring instrument technologies use software applications in order to determine measured quantity values. The results provided by computer programs are not to be trusted blindly. Their diversity and complexity frequently make such computer programs error-prone. Even comprehensive tests conducted by the manufacturer cannot offer a guarantee that all “errors” have been found. Therefore, it is even more important to validate the

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software in order to prove that it meets the demands for the application in practice and that all relevant information is displayed completely. In order that software applications provide a very high level of correct results, several standards demand validation of the applied software: • Extract from DIN EN ISO 9001 [11] or ISO/TS 16949 [23] Chapter 7.6 “Control of monitoring and measuring equipment” By using computer software for monitoring and measuring specified requirements, the suitability of this software for the intended use must be confirmed. This confirmation must be provided prior to initial use and, where necessary, repeated later on. • Extract from ISO 10012 [12], Chapter 6.2.2 “Software”: Software used in the measurement processes and calculations of results shall be documented, identified and controlled to ensure suitability for continued use. Software, and any revision to it, shall be tested and/or validated prior to initial use, approved for use and archived. The typical range of the various computer programs used for monitoring and measuring specified requirements include measurement and evaluation programs for: •

coordinate measuring machines

•

measuring forms and surfaces

•

measuring systems / SPC systems

•

test benches

•

statistical evaluations

The demands on computer programs apply to third-party software and to the corporate software. A standardized procedure is recommended for an efficient validation. The validation shall be documented by means of an individual checklist. This list shall contain a reference to the following tasks, for example: • Compare release number on data storage medium to manual / information. • Document individual configuration and settings of the software. • Check important functions (to be specified for each respective application) after installation is completed.

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• Take measurements on calibrated reference standards and compare results to the calculated actual values and to the results of the previous version (also considering measurement uncertainty). • Check whether all relevant information is provided. • Compare results (e.g. obtained from multiple point measuring instrument) with more precise measuring system (e.g. coordinate measuring machine in measuring laboratory). • In order to make an evaluation, test data shall be provided with known results. This data is loaded, recalculted and the results are compared to the results of references. After completing the vaildation successfully: • approve the program explicitly for use. • Replace/update all installed systems concerned (if possible via network in order not to miss any individual system). • inform the users concerned about the latest software version. • sign a software maintenance contract, if possible, in order to be informed about any future upgrades (e.g. new guidelines, standards and legal regulations) automatically. Naturally, software is not subject to wear. For this reason, no further inspections of the validated software are required while it is used. However, the software must be validated again when changes in the system environment or to any signficant charcateristics of the software, hardware or the operating system take place. Ideally, the software producer / supplier provides a certificate of qualification (expert opinion).

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9

Capability Analysis of Attribute Measurement Processes

9.1

Introduction

Because of the nature of attributive measurements, it is only possible to obtain a satisfactory outcome regarding the capability of attribute measurement processes with a great deal of effort. A suitable approach for calculating the capability of attribute measurement processes must take into account that the probability of a particular test result is dependent on the type of characteristic. Hence, it is all about conditional probabilities. P (test result | value of the characteristic) The probability of a correct test result is nearly 100% for the values of the characteristic that lie beyond the areas of uncertainty around the specification limits. This probability is approximately 50% if the measurement results lie in the middle of the uncertainty range ("a decision by pure chance"). In principle, the proposed approach makes a distinction between the calculation of measurement capability without, or with reference values. In the case that reference values are available, a two-step approach is proposed.

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9.2

Capability Calculations without Using Reference Values

In this case, only a test of whether there are significant differences between operators can be made. But an assessment of whether the test has led to the correct result cannot be taken. However, this fact must always be considered when no reference values are present. The choice of test parts may have a decisive influence on the outcome of this test method, but it cannot be taken into account in this case. The following standard experiment is proposed: At least 40 different test parts should be tested 3 times by 2 different operators, called A and B. Each of the different measurement results on the 40 parts, which the operator A or operator B has achieved, is assigned to one of the following three classes. Class 1: Class 2: Class 3:

All three test results on the same part gave the result "good". The three test results on the same part gave different results. All three test results on the same part gave the result "bad".

The test results can be summarized in a table. Frequency nij

Operator A

Class 1 result “+++“ Class 2 different results Class 3 result “- - -“

Class 1 result “+++“

Operator B Class 2 different results

Class 3 result “- - -“

7

3

1

10

4

7

2

1

5

This table is now tested using a Bowker-Test of symmetry. If there are no significant differences between operators, the resulting frequencies nij in the above table will be sufficiently symmetrical with respect to the main diagonal.

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The hypothesis H0: mij = mji (i, j = 1, …, 3 where i ≠ j) says that the expected frequencies mij which lie symmetrical with respect to the main diagonal are identical. The test value

χ2 = ∑ i> j

(nij - n ji )2 nij + n ji

= 8,603

is compared to the test statistic with 3 degrees of freedom. The hypothesis on symmetry is rejected on the level if the test value is greater than the quantile in the χ² distribution with 3 degrees of freedom. Bowker-Test of symmetry of the expected frequencies Null hypothesis H0:

mij = mji (i, j = 1, …, 3 where i ≠ j) both operators obtain similar results

Alternate hypothesis H1:

mij ≠ mji both operators obtain different results

Test value:

χ2 = ∑ i> j

Test statistic:

Test decision:

(nij - n ji )2 nij + n ji

= 8,603

1-α fractile χ²1-α ; 3 quantile ---------------------------------------------0,90 6,251 0,95 7,815 0,99 11,345 0,999 16,266 The null hypothesis H0 is rejected with an error probability of α ≤ 5% because the calculated test value is greater than the test statistic, which is the 95 % fractile of the distribution.

Conclusion: The results of the two operators can be regarded as different.

In principle, this method is also to be used with more than 2 operators. In such cases, all operators take 3 repeatability tests on the test part and subsequently, all combinations of two combinations of operators should be tested individually. One should note that in this case the significance level is changed for the overall statements by these multiple tests.

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9.3

Capability Calculations Using Reference Values

9.3.1

Calculation of the Uncertainty Range

The signal detection approach requires test parts with known reference values. The purpose of the method is to determine the uncertainty range, in which an operator is unable to make an unambiguous decision. The following numeric example is taken from the MSA manual [1] where two further methods are explained that are not examined in this document.

for the last time corresponding „Rejection“ d = 0,566152 − 0,542704 U

= 0,023448

for the first time corresponding „Acceptance“

for the last time corresponding „Acceptance“ d = 0,470832 − 0,446697 U

= 0,024135 for the first time corresponding „Rejection“

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Symbols In the table, the reference measurement values are introduced in the form of a code. A plus sign means that all three operators have indicated the result from the test part as approved in all three tests, and that this assessment is consistent with the reference value. A minus sign means that all three operators have indicated the result from the test part as not approved in all three tests and that this assessment is consistent with the reference value. The symbol “X” indicates a case where at least one of the operators has come to a test result, which is not consistent with the reference value.

Working steps for determining the uncertainty range: Step 1: Sort the table according to the measured reference size. In the above example, a sorting in descending order is made - from the highest reference value descending to the lowest reference value. Step 2: Select the last reference value for which all operators have assessed all the results as being unsatisfactory (symbol “-“). This is the transition from symbol "–" to symbol "X". 0,566152 0,561457

X

Step 3: Select the first reference value for which all operators the first time assessed all results being approved (symbol “+”). This is the transition from symbol "X" to the symbol "+". 0,543077 0,542704

X +

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Step 4: Select the last reference value for which all operators last time assessed all the results as being approved (symbol ”+“). This is the transition from the "+" symbol to the symbol "X". 0,470832 0,465454

+ X

Step 5: Select the first reference value for which every operator has again first assessed all the results as unsatisfactory (symbol “-“). This is the transition from symbol "X" to the symbol "–". 0,449696 0,446697

X -

Step 6: Calculate the dU interval from the last reference value, for which all operators have assessed the result as unsatisfied to the first reference value, for which all operators have the result as approved. dU = 0,566152 – 0,542704 = 0,023448 Step 7: Calculate the dL interval from the last reference value, for which all operators have assessed the result as approved to the first reference value, for which all operators have the result as unsatisfied. . dL = 0,470832 –0,446697 = 0,024135 Step 8: Calculate the average d of the two intervals. d = (dU + dL) / 2 = (0,023448 + 0,024135) / 2 = 0,0237915

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Step 9: Calculate the uncertainty range. UATTR = d / 2 = 0,0237915 / 2 QATTR = 2 · UATTR / TOL = 2 ·( 0,0237915 / 2) / 0,1 ≈ 0,24 Then QATTR amounts to about 24 %.

Figure 23: Value chart plotting all reference values and the calculated uncertainty range Remark:

The effort for this method is considerable, as in this example in addition to the 50 reference measurements also at least 450 other test measurements have to be made and documented.

For the selection of test parts, it must be presumed that the uncertainty range will be covered. A maximum of the half tolerance must be covered around the specification limits. This region can be limited due to available information and by considering the resolution. A measurement process capability analysis requires that the limits of the real uncertainty range are determined.

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9.3.2

Ongoing Review

For ongoing review of the measurement process, at least one operator should measure at least 3 test parts all with defined reference values. The test parts should be selected in a way that the reference values are located within the zone I, II or III so that a clear result can be expected (all tests are consistent with the reference value).

UMP

UMP

UMP

UMP

The size of the uncertainty range can either be determined experimentally (see previous chapter), or derived from the actual defined requirements for an appropriate measurement process (QMP).

QMP =

2 UMP ⋅ 100% ≤ QMP _ max TOL

This leads to

UMP _ max =

QMP _ max ⋅ TOL 2 ⋅ 100%

It is to be taken into account that the extended uncertainty is usually given to be the 95,45 % level.

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10

Appendix

Annex A

Statistical Background of the Measurement Process Capability Analysis

Annex A.1

Formulas for Calculating the Regression Function

y nk = β0 + β1 ⋅ xn + ε nk Formulas for estimating the unknown parameters β0 (“y-intercept“) and β1 (”slope“): N

βˆ 1 =

∑ (x

n

- x ) ⋅ ( yn - y )

n -1

N

∑ (x

n

- x )²

n -1

βˆ0 = y − βˆ1 ⋅ x and the residuals enk: N

σˆ ² = where ynk

K

N

K

∑∑ (enk ) ² ∑∑ (ynk - yˆn ) ² n -1 n -1

N ⋅K - 2 th

=

n -1 n -1

where

N ⋅K - 2

yˆ n = βˆ0 + βˆ1 ⋅ x n

th

k of K measurements on the n of N standards th

xn

conventional true value for the n standard

εnk

N(0,σ ) distributed deviations of ynk from the expected value th (β0+β1·xn) obtained in the measurement on the n standard

2

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Annex A.2

ANOVA Tables

Since the uncertainty components affect the measurement results in the form of random errors (see Chapter 4.1), only ANOVA analyses of model II (random components of uncertainty only) are considered. Analysis of variance table referring to Chapter 5.2.2.2 LIN = linearity EVR = repeatability on standards

N = number of standards K = number of repetitions yn • = ∑ ynk

Mean of the values measured on standard n

yn • =

k

ynk K

Sum of squares

Degrees of freedom

LIN

SSLIN = ∑∑ ( ynk - yˆn ) ² - SSEVR

fLIN = N - 2

MSLIN =

SSLIN fLIN

EVR

SSEVR = ∑∑ ( ynk - yn • ) ²

fEVR = NK - N

MSEVR =

SSEVR fEVR

n

k

n

k

Mean square

Estimated variance

Estimated standard deviation

Test statistic F (F-Test)

Critical value F0

LIN

σˆ ² LIN = MS LIN

σˆ LIN = σˆ ²LIN

MSLIN MSEVR

F (1 - α , f LIN , f EVR )

EVR

σˆ ² EVR = MS EVR

σˆ EVR = σˆ²EVR

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Analysis of variance tables referring to Chapter 5.3.1 AV = operator’s reproducibility

NA = number of operators

PV = reproducibility part to part

NP = number of parts

IA = interaction operator - part

NR = number of repetitions

EVO = repeatability on parts

Case 1: Uncertainty components from repeatability

yp • =

Mean of the values measured on part p

y

••

SSPV = NR ∑ ( yp • − y

••

)²

SSEVO = ∑∑ ( ypr - yp •) ² p

r

Estimated variance

EVO

σˆ ²PV =

∑∑y

pr

r

Degrees of freedom

p

PV

=

p

Sum of squares

EVO

yp • NR y •• y •• = NRNP yp• =

pr

r

Overall mean

PV

∑y

fPV = NP − 1 fEVO = N P (NR - 1)

Estimated standard deviation

SSPV fPV SSEVO MSEVO = fEVO MSPV =

Test statistic F (F-Test)

Critical value F0

MSPV MSEVO

F (1- α , f PV , f EVO )

MSPV − MSEVO NR

σˆ ²EVO = MSEVO

Mean square

σˆ EVO = σˆ ²EVO

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Case 2:

Uncertainty components from operator, repeatability and interactions between operator and part

Mean of the values measured on part p by operator a

yap• = ∑ yapr

Mean of the values measured by operator a

ya • • = ∑∑ yapr r

y

Mean of the values measured on part p

∑∑y

=

• p•

p

a

a

p

Sum of squares

SSAV = NRNP ∑ ( ya • • − y

PV

SSPV = NRNA ∑ ( y

IA

SSIA = NR ∑∑ ( yap • − ya • • − y

• p•

fAV = NA − 1

MS AV =

SS AV fAV

•••

)²

fPV = NP − 1

MS PV =

SS PV fPV

f IA = (N A − 1)(N P − 1)

MSIA =

SSIA fIA

f EVO = N A N P (N R - 1)

MSEVO =

a

•p•

+y

p

SSEVO = ∑∑∑ (yapr − yap ) ² a

p

r

AV

σˆ ² AV =

MSAV − MSIA NPNR

PV

σˆ ²PV =

MSPV − MSIA NANR

IA

σˆ ²IA =

96

−y

MSIA − MSEVO NR

σˆ ² E VO = MS E VO

Mean square

)²

•••

a

Estimated variance

EVO

r

Degrees of freedom

AV

EVO

apr

r

y • •• = ∑∑∑ yapr

Overall mean

a

yap • NR ya • • ya • • = NRNP y •p• y • p• = NRNA y ••• y • •• = NR NA N P yap • =

r

•••

)²

SS EVO fEVO

Estimated standard deviation

Test statistic F (F-Test)

Critical value F0

σˆ AV = σˆ ² AV

MS AV MS IA

F (1 - α , f AV , f IA )

MS PV MS IA

F (1 - α , f P V , f IA )

MSIA MSEVO

F (1- α , f IA, fAVO )

σˆ IA = σˆ ²IA σˆ EVO = σˆ ²EVO

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If the interaction between the operator and the part is not significant, i.e. if F < F0, repeatability and interaction should be combined to a single component (pooling). Then:

MSPool =

SSPool fEVO + fIA

•

SSPool = SSEVO + SSIA

•

MSPool replaces MSIA in the AV and PV line of the variance table.

•

The estimated standard deviation from repeatability is

and

σˆ EVO = MSPool Case 3: Uncertainty components from measuring system, repeatability and interaction between measuring system and part Similar to case 2, but replacing the operator by the measuring system.

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Annex B

Estimation of Standard Uncertainties from Temperature

Since most materials change as the temperature varies, the standard uncertainty from temperature uT must be determined in all measurements (Figure 24).

Figure 24: Determining the standard uncertainty from temperature uT

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In comparing a test part (work part) to a reference standard or a scale, temperature variations do not affect the measurement result if the test part and the reference standard or scale are made of the same material and have the same temperature. If this is not the case, the measurement result is subject to an uncertainty caused by different expansion coefficients. Since these temperature variations can be quite high, the results should generally be corrected for these variations mathematically (compensation for temperature difference). Annex B.1 Uncertainty with Correction of Different Linear Expansions The calculation of corrected measured quantity value ycorr depends on the type of measurement: Absolute measurement

y corr = where

yi ∆TOBJ ∆TR

αOBJ αR

y i ⋅ (1 + α R ⋅ ∆TR ) 1 + αOBJ ⋅ ∆TOBJ

B.1

= value displayed by the measuring instrument = test part’s deviation of temperature from 20° C = reference standard’s deviation of temperature from 20° C = thermal expansion coefficient of test part = thermal expansion coefficient of reference standard (e.g. glass scale of a height gauge)

If a good approximation is available, the following formula applies:

y corr ≈ y i ⋅ 1 − (α OBJ ⋅ ∆TOBJ − α R ⋅ ∆TR )

B.2

Comparison measurement

y corr =

y R ⋅ (1 + α R ⋅ ∆TR ) + d 1 + αOBJ ⋅ ∆TOBJ

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B.3

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where : d yR

= =

∆TR

=

αR

=

temperature difference (test part – reference standard) length of reference standard at reference temperature of 20° C reference standard’s deviation of temperature from 20° C thermal expansion coefficient of reference

If a good approximation is available, the following formula applies:

y corr ≈ y R + d + y R (α R ⋅ ∆TR − αOBJ ⋅ ∆TOBJ )

B.4

Since the (measured) temperatures and the thermal expansion coefficients used in the calculation also cause an uncertainty, an uncertainty from other influence components uREST remains. Assuming that αOBJ , αR , ∆TOBJ and ∆TR are uncorrelated and that there are no changes in temperature during the measurement, the standard uncertainty from temperature is calculated by: 2 2 uT = uREST = y i ; y R ∆TR2uα2R + ∆TOBJ uα2OBJ + α R2 u∆2TR + αOBJ u∆2TOBJ

B.5

In case no further data is available, the uncertainty from expansion coefficients is assumed to be 10 % of these coefficients and the uncertainty from temperature amounts to 1 Kelvin. If temperature variations (drifts) might occur during the measurement, these influences must possibly also be considered. As an example, Table B.1 lists uncertainties from other influence components caused in measurements on test parts made of different materials and by using different scales or reference standards. All these examples are based on the assumption that the temperature of the test part and the measuring instrument is nearly the same (test part has been controlled) and that the temperature is constant during the measurement. It is also assumed that uα = 1 Kelvin . = 0,1⋅ αOBJ;R and u∆T OBJ ;R

100

OBJ ;R

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System without

expansion α R ≈ 0 1/K

Glass

Ceramic

Steel

Gauge Standard

Material of the test part

Aluminium -6 αOBJ = 24 10 1/K Brass -6 αOBJ = 18 10 1/K Steel -6 αOBJ = 11,5 10 1/K Cast iron -6 αOBJ = 10 10 1/K Aluminium -6 αOBJ = 24 10 1/K Brass -6 αOBJ = 18 10 1/K Steel -6 αOBJ = 11,5 10 1/K Cast iron -6 αOBJ = 10 10 1/K Aluminium -6 αOBJ = 24 10 1/K Brass -6 αOBJ = 18 10 1/K Steel -6 αOBJ = 11,5 10 1/K Cast iron -6 αOBJ = 10 10 1/K Aluminium -6 αOBJ = 24 10 1/K Brass -6 αOBJ = 18 10 1/K Steel -6 αOBJ = 11,5 10 1/K Cast iron -6 αOBJ = 10 10 1/K

Table B.1:

Uncertainty from other influence components uT in µm per 100 mm with a temperature deviation ∆TOBJ;R from 20° C 0K

2,5 K

5K

7,5 K

10 K 12,5 K 15 K

2,7

2,7

3,0

3,3

3,8

4,3

4,8

2,1

2,2

2,4

2,7

3,0

3,4

3,9

1,6

1,7

1,8

2,0

2,3

2,6

2,9

1,5

1,6

1,7

1,9

2,2

2,4

2,7

2,6

2,7

2,9

3,2

3,7

4,1

4,7

2,0

2,1

2,3

2,5

2,9

3,3

3,7

1,5

1,5

1,7

1,9

2,1

2,4

2,7

1,4

1,4

1,5

1,7

2,0

2,2

2,5

2,5

2,6

2,8

3,2

3,6

4,0

4,6

2,0

2,0

2,2

2,5

2,8

3,2

3,6

1,4

1,4

1,6

1,8

2,0

2,2

2,5

1,3

1,3

1,4

1,6

1,8

2,1

2,3

2,4

2,5

2,7

3,0

3,4

3,8

4,3

1,8

1,9

2,0

2,3

2,5

2,9

3,2

1,2

1,2

1,3

1,4

1,6

1,8

2,1

1,0

1,0

1,1

1,3

1,4

1,6

1,8

Standard uncertainty uT from test parts made of different materials using different scales or reference standards in case a compensation for temperature difference is made (in this table K stands for Kelvin)

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Annex B.2

Uncertainty without Correction of Different Linear Expansions

Since most cases occurring in practice do not allow for a correction by calculation, errors that are caused by different expansions at temperatures deviating from 20° C must also be considered. The following procedure is based on the assumption that the temperature of the test part and the measuring instrument is nearly the same during the measurement (test part has been controlled) and that a specified maximum temperature deviating from 20° C is not exceeded. The greatest possible measurement error that can occur at a maximum temperature tmax is regarded as the error limit a caused by temperature influences. Note 1:

This approach particularly applies to temperature-controlled measuring laboratories where the actual temperature is stable between a reasonable maximum and a minimum temperature around the reference temperature of 20° C.

Note 2:

If a high maximum temperature is permissible, its resulting uncertainty component frequently makes up a major part of the uncertainty budget and often causes an unsatisfactory expanded measurement uncertainty UMP that is extremely high.

Due to different linear expansions at the maximum temperature tmax, the measurement error ∆yi, in case of a good approximation, is calculated by:

∆y ≈ y i ; y R ⋅ (t max − 20 ° ) ⋅ (α OBJ − α R )

B.6

This measurement error is added to the uncertainty from different expansion coefficients αR or αOBJ (at tmax) and leads to the maximum permissible error a (worst case) caused by temperature variations.

a = ∆y i + 2u REST

where

2 uREST = y i ; y R ⋅ ∆TR2 ⋅ uα2R + ∆TOBJ ⋅ uα2OBJ

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Thereby uREST is calculated as described in formula B.5, but leaving out the uncertainty components of the temperature measurement that was not tak2 en in this case ( α R2 ⋅ u∆2T = 0 and αOBJ ⋅ u∆2T = 0 ). R

OBJ

This leads to the standard uncertainty from temperature:

uT =

a

B.8

3

As an example, Table B.2 lists uncertainties from other influence components caused in measurements on test parts made of different materials using different scales or reference standards when the different linear expansions where not corrected by calculation. It is assumed that uα = 0,1⋅ αOBJ;R . OBJ ;R

Note 1:

Strictly speaking, the uncertainty calculated by the methods described above only applies to rod-shaped test parts with a homogenous temperature. By contrast, it is difficult to estimate the thermal expansion and thus the uncertainty from expansion coefficients for any other, particularly asymmetric test parts. However, the uncertainty generally only becomes smaller compared to the rod-shaped test part so that one is always “on the safe side”.

Note 2:

The tables show that a different thermal expansion coefficient of the test part and the reference standard result in high uncertainties. This leads to the conclusion that measuring instruments including scales with very small thermal expansion coefficients cause a high measurement uncertainty if a compensation for temperature difference is not made. In general, these measuring instruments require a correction of temperature influences by calculation.

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Gauge

Standard

Steel Ceramic

Aluminium -6 αOBJ = 24 ⋅ 10 1/K Brass -6 αOBJ = 18 ⋅ 10 1/K Steel -6 αOBJ = 11,5 ⋅ 10 1/K Cast iron -6 αOBJ = 10 ⋅ 10 1/K Aluminium -6 αOBJ = 24 ⋅ 10 1/K Brass -6 αOBJ = 18 ⋅ 10 1/K Steel -6 αOBJ = 11,5 ⋅ 10 1/K Cast iron -6 αOBJ = 10 ⋅ 10 1/K Aluminium -6 αOBJ = 24 ⋅ 10 1/K Brass -6 αOBJ = 18 ⋅ 10 1/K Steel -6 αOBJ = 11,5 ⋅ 10 1/K Cast iron -6 αOBJ = 10 ⋅ 10 1/K Aluminium -6 αOBJ = 24 ⋅ 10 1/K Brass αOBJ = 18 ⋅ 10-6 1/K Steel -6 αOBJ = 11,5 ⋅ 10 1/K Cast iron -6 αOBJ = 10 ⋅ 10 1/K

System without expansion α R ≈ 0 1/K

Glass

Material of the test part

Table B.2:

104

Uncertainty from other influence components uT in µm per 100 mm with a temperature deviation ∆TOBJ;R from 20° C 0,5 K

1K

2,5 K

5K

7,5 K

10 K

15 K

0,5

1,0

2,6

5,1

7,7

10,3

15,4

0,3

0,6

1,6

3,1

4,7

6,2

9,3

0,1

0,2

0,5

0,9

1,4

1,9

2,8

0,1

0,3

0,7

1,3

2,0

2,6

3,9

0,6

1,1

2,8

5,7

8,5

11,4

17,0

0,4

0,7

1,8

3,6

5,4

7,3

10,9

0,1

0,3

0,7

1,4

2,2

2,9

4,3

0,1

0,2

0,5

0,9

1,4

1,9

2,8

0,6

1,2

3,0

6,1

9,1

12,2

18,2

0,4

0,8

2,0

4,0

6,0

8,0

12,1

0,2

0,4

0,9

1,8

2,7

3,6

5,5

0,1

0,3

0,7

1,3

2,0

2,6

4,0

0,8

1,7

4,2

8,3

12,5

16,6

24,9

0,6

1,2

3,1

6,2

9,4

12,5

18,7

0,4

0,8

2,0

4,0

6,0

8,0

12,0

0,3

0,7

1,7

3,5

5,2

6,9

10,4

Standard uncertainty uT from test parts made of different materials using different scales or reference standards in case a compensation for temperature difference is not made (in this table K stands for Kelvin)

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Annex C

Reducing the Measurement Uncertainty by Repeating and Averaging Measurements

The measurement uncertainty can be reduced by repeating and averaging measurements. By taking repeated measurements instead of an individual measurement, the random measurement uncertainty components can be reduced by a factor of n * . Prior to that, the standard uncertainty must be determined based on 25 repeated measurements under equal conditions of measurement, i.e. the standard deviation of a previous series of measurements is used in order to express the measurement uncertainty (cf. Chapter 5). The figure below shows how raising the number of measured quantity values n* reduces the standard uncertainty. 100

extension of measurement uncertainty

80

60

40

20

0 1

5

9

13

17

21

25

No. of measurements

Figure A.D.1: Reducing the measurement uncertainty by raising the number of repeated measurements n*

In case of an individual measurement of a characteristic, the experimentally determined repeatability of the measuring instrument is included in the uncertainty budget in the form of uEVR or uEVO (cf. Chapter 5.2 and 5.3). If a measurement result is obtained by repeating and averaging the measurement of one characteristic, the influence of the variation is reduced. The uncertainty component from repeatability on test parts is not calculated from the variation of individual measured quantity values but from the smaller random variation of the means of these measured values.

uEVO * =

uEVO n*

.

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where n* is the number of measurements required for averaging the measurement. In the uncertainty budget, the uncertainty uEVO* replaces the uncertainty uEVO that was determined experimentally during the capability analysis. It is important to consider that only the greatest value of uEVR, uEVO or uRE is considered in the uncertainty budget. For this reason, the standard uncertainty from repeatability on standards uEVR must always be replaced by uEVR* which is reduced by a factor of n * . It must also be compared to the uncertainty from resolution of the measuring system uRE. Example: An experiment led to the following uncertainty budget: uCAL = 0,8 µm, uEVR = 0,9 µm, uEVO = 1,1 µm, uRE = 0,6µm, uAV = 1,3 µm measured quantity value of individual measurement: ø 20,354 mm The combined standard uncertainty

{

}

2 2 2 2 2 uMP = uCAL + max u EVR ;uEVO ;uRE + u AV

is calculated using the uncertainty components listed above: 2 2 2 uMP = uCAL + uEVO + u AV = 0,8 2 + 1,12 + 1,3 2 = 1,88 µm.

measurement result: ø 20,354 mm ± 3,76 µm (k=2). measured quantity values of repeated measurement: ø 20,354 mm; ø 20,348 mm; ø 20,352 mm Based on n* = 3 repeated measurements, the uncertainty amounts to uEVO* = 1,1 3 = 0,64 µm or uEVR* = 0,9 3 = 0,52 µm, whereby uMP is reduced 2 2 2 uMP = uCAL + uEVO 0,8 2 + 0,642 + 1,3 2 = 1,66 µm. * + u AV = measurement result: ø 20,3513 mm ± 3,32 µm (k=2). If the number of repeated measurements is raised once again, e.g. to n* = 5, the uncertainty is even more reduced uEVO* = 1,1 5 = 0,49 µm or uEVR* = 0,9 5 = 0,40 µm. However, this does not lead to a considerable improvement of the measurement result because the uncertainty from resolution uRE = 0,6 is the greatest uncertainty component. Thus, it is the only component of the measuring instrument to be considered in the result. 2 2 2 uMP = uCAL + uRE + uAV = 0,82 + 0,62 + 1,32 = 1,64 µ m .

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Annex D

k Factors

If the specified design of experiments cannot be realized in terms of the demanded sample size, it is necessary to take a Student t-distribution instead of the standard normal distribution to estimate the uncertainty components. This will then result in the expanded measurement uncertainty: U MP = t f ,1-α / 2 ⋅ u MP The number of degrees of freedom f is obtained from the product of the number of test parts, the number of operators, the number of measuring systems and the number of repeatability measurements reduced by one. For

f = 3 ⋅ 2 ⋅ 2 ⋅ (3 − 1) = 24 one will find

t 24,1-α / 2 = 2,11 ,

For

f = 3 ⋅ 2 ⋅ 2 ⋅ (2 − 1) = 12 one will find

t12,1-α / 2 = 2,23 .

degree of freedom f k values (p=95,45%)

Table 15:

1

2

3

4

5

6

7

8

9

10

11

12

13

14 → ∞

13,97 4,53 3,31 2,87 2,65 2,52 2,43 2,37 2,32 2,28 2,25 2,23 2,21 2,20 2,0

k values for a 95,45% level of confidence according to the respective degree of freedom

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Annex E

Setting Working Point(s)

Before a measuring system can be applied for measurements, it must normally be set using one or two reference standard(s). The measuring system is set according to the calibrated actual value of the standard (working standard) which makes the system ready for use. Depending on the measurement procedure or measuring system, there are different methods available in order to set the system. Setting a working point using a calibrated reference standard Determination of the systematic measurement error and the repeatability (Type 1 study): display

real

ideal y = x

b

0

measured value

This method is applied to linear measuring systems for setting the working point. The value of the reference standard shall lie within an area of +/-10 % around the working point.

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Setting working points using two calibrated reference standards Determination of the systematic measurement error and the repeatability (Type 1 study):

Case 1 display

This method is applied to linear measuring systems for setting zero on the system or for boosting. The values of the reference standard shall lie within an area of +/-10% around the zero point and the upper working point. The uncertainty components are determined from the repeatability variation on the reference standards and from the deviations of the calculated means from the calibrated actual values of the reference standards (using the greatest value in each case). Case 2

real

ideal y = x 2. reinforcement set gradient

1. set zero point 0

measured value

display

This method is used in order to set the upper and lower specification limit on the measuring system. The values of the reference standard shall lie within an area of +/-10% around the limits. The uncertainty components are determined from the repeatability variation on the reference standards and from the deviations of the calculated means from the calibrated actual values of the reference standards (using the greatest value in each case).

real

ideal y = x

0

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L

U

measured value

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Annex F

Calculation Examples

Annex F.1

Measurement Process Capability Using 3 Standards

An instrument measuring boltholes requires that the capability of the measurement process for inside diameters should be established and documented. Uncertainties from test part or the temperature are regarded as negligible and are not considered in the evaluation. Information about measuring system and measurement process Nominal dimension

30,000 mm

Upper specification limit U

30,008 mm

Lower specification limit L

30,003 mm

Resolution of the measuring system RE (1 digit = 0,0001mm)

0,1 µm

Calibration uncertainty UCAL

0,026 µm

Coverage factor kCAL

2

Linearity

0

Reference quantity value of the standard at the upper specification limit xmu

30,0076 mm

Reference quantity value of the standard in the centre of the specification xmm

30,0050 mm

Reference quantity value of the standard at the lower specification limit xml

30,0025 mm

Capability ratio limit measuring system QMS_max

15%

Capability ratio limit measurement process QMP_max

30%

In order to determine the standard uncertainties from repeatability on standards and from measurement bias, an experiment was conducted performing 10 repeated measurements on each of 3 reference standards.

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The information about the measuring system and the measured quantity values gained in the experiment leads to the following uncertainty budget and overview of results.

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Due to a percentage resolution %RE of 2,00% and a capability ratio QMS of 7,86%, the capability of the measuring system of the instrument measuring boltholes is established. After the capability of the measuring system is established, the measurement process is analyzed. The operator influence, the repeatability on test parts and their interactions are determined experimentally under operational conditions. In this experiment, 2 repeated measurements are performed on each of 10 test parts by 3 operators.

Based on the recorded measured quantity values, the individual standard uncertainties can be determined and allocated by using the method of ANOVA. This leads to the following uncertainty budget and overview of results for the measurement process.

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Due to a capability ratio QMP of 14,98% in case of a process capability ratio limit QMP_max of 30%, the capability of the measurement process of the instrument measuring boltholes is established.

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Annex F.2

Process Capability Using a D-optimum Design

Analogous to the example in Annex F.1, a new measurement process capability analysis should be made for the instrument measuring boltholes. However, in this case, the additional uncertainty component caused by the test part influence shall be considered. It is determined by taking further measurements at 4 different measuring points of the inside diameter. In order to minimize the effort for this experiment, the experiment is reduced to a minimum of measurements with the help of a D-optimum experimental design. The specifications, measured quantity values and results of the measuring system are the same as in the example of Annex F.1 and can be transferred to this example. For the measurement process, a D-optimum experimental design is created including 2 repeated measurements at each of 4 measuring points of 10 test parts by 3 operators. The D-optimum experimental design reduces the effort involved from 240 to 128 individual measurements. These are taken in random combinations of operator/test part/measuring point and evaluated by using the method of ANOVA. The information about the measuring system (see Annex F.1) and the measured quantity values of the D-optimum experimental design lead to the following uncertainty budget and overview of results.

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Table 16:

Measured quantity values of the D-optimum experimental design

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Due to a capability ratio QMP of 20,38% in case of a process capability ratio limit QMP_max of 30%, the capability of the measurement process of the instrument measuring boltholes is established.

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Annex F.3

Measurement Process Capability of a CMM

Measuring the inside diameter of a pump housing on a reference standard by using a coordinate measuring machine requires that the capability of the measurement process is established and documented.

Information about measuring system and measurement process Nominal dimension

150,00 mm

Upper specification limit U

150,02 mm

Lower specification limit L

149,98 mm

Resolution of the measuring system RE (1 digit = 0,0001mm)

0,1 µm

Reference quantity value of the standard

150,0015 mm

Calibration uncertainty UCAL

2 µm

Coverage factor kCAL

2

Linearity

0

Capability ratio limit measuring system QMS_max

15%

Standard uncertainty from expansion coefficients of the test part uαOBJ

1 10 /K

Mean temperature of the measurement process

22° C

Value displayed by measuring system

150,00 mm

Capability ratio limit measurement process QMP_max

30%

-6

In order to determine the standard uncertainties from repeatability on standards and from measurement bias, 20 repeated measurements were performed on a reference standard. Since the linearity deviation is zero, the linearity can be neglected.

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The information about the measuring system and the measured quantity values gained in the experiment lead to the following uncertainty budget and overview of results.

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Due to a percentage resolution %RE of 0,25% and a capability ratio QMS of 14,42%, the capability of the measuring system of the CMM is established. Since the measurement process capability only refers to one reference standard and a CMM does not involve a classical operator influence, the uncertainty from temperature is considered for this measurement process as described in ISO/TS 15530-3 [16]. This leads to the following uncertainty budget and overview of results for the measurement process.

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Due to a capability ratio QMP of 14,73% in case of a process capability ratio limit QMP_max of 30%, the measurement process capability of the CMM for measuring the inside diameter on a reference standard is established.

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Annex F.4 Measurement Process Capability of Automated Test Device The measurement process capability of automated test device must be established and documented. Information about measuring system and measurement process Nominal dimension

53,01 mm

Upper specification limit U

53,03 mm

Lower specification limit L

52,99 mm

Resolution of the measuring system RE (1 digit = 0,0001mm)

0,5 µm

Calibration uncertainty UCAL

1,6 µm

Coverage factor kCAL

2

Linearity uLIN (from preliminary investigation)

0

fmax of dial gauge (MPE)

1,2 µm

Reference quantity value of standard

53,0105 mm

Capability ratio limit of measuring system QMS_max

15%

Expansion coefficient α of test part for steel

11,5 1/K ⋅ 10 /K

Expansion coefficient α of measuring system for steel

11,5 1/K ⋅ 10 /K

Standard uncertainty from expansion coefficients of test part uαOBJ for steel

1,2 1/K ⋅ 10 /K

Standard uncertainty from expansion coefficients of measuring system uαR for steel

1,2 1/K ⋅ 10 /K

Maximum temperature (environment)

25° C

Delta temperature of working standard at 20°C

5° C

Delta temperature of working standard at 20°C

10° C

Value displayed by measuring system

53 mm

Capability ratio limit measurement process QMP_max

30%

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-6

-6

-6

-6

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measured at system level

In order to determine the standard uncertainties from repeatability on standards and from measurement bias, 25 repeated measurements were performed on the reference standard. A preliminary investigation did not detect any linearity deviations, so linearity must not be considered.

The information about the measuring system and the measured quantity values gained in the experiment lead to the following uncertainty budget and overview of results.

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Due to a percentage resolution %RE of 1,25% and a capability ratio QMS of 11,54%, the measuring system capability of the automated measuring equipment is established.

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After observing the measuring system, the measurement process of the automated test device is analyzed. In an experiment, 2 repeated measurements are performed on each of 10 test parts.

In addition to the repeatability on test parts, the temperature influence must also be considered. It is calculated from the difference between the expansion of the working standard and the test part and from the general uncertainty from temperature without correcting the linear expansion. This leads to the following uncertainty budget and overview of results.

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Due to a capability ratio QMP of 21,67% in case of a process capability ratio limit QMP_max of 30%, the measurement process capability of the automated test device is established.

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Annex F.5 Measurement Process Capability of a Multiple-point Measuring Instrument The measurement process capability for a multiple-point measuring instrument with 3 equal measuring points must be established and documented. First, the measuring system is observed by considering the influence factors of resolution, calibration uncertainty on standards, repeatability on standards, bias and sensor/touching as additional uncertainty components. Information about measuring system Nominal dimension

64,505 mm

Upper specification limit U

64,530 mm

Lower specification limit L

64,480 mm

Resolution of the measuring system RE (1 digit = 0,0001mm)

0,1 µm

Calibration uncertainty UCAL

1,8 µm

Coverage factor kCAL

2

Linearity uLIN (from preliminary investigation)

0

Error limit of sensor / by touching

0,8 µm

Reference value standard 1/meas. point 1

64,5042 mm

Reference value standard 1/meas. point 2

64,5035 mm

Reference value standard 1/meas. point 3

64,5016 mm

Reference value standard 2/meas. point 1

64,5421 mm

Reference value standard 2/meas. point 2

64,5449 mm

Reference value standard 2/meas. point 3

64,5465 mm

Reference value standard 3/meas. point 1

64,4604 mm

Reference value standard 3/meas. point 2

64,4612 mm

Reference value standard 3/meas. point 3

64,4596 mm

Capability ratio limit of measuring system QMS_max

15%

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Information about measurement process -6

Expansion coefficient α of test part for steel

11,5 1/K 10 /K

Expansion coefficient α of measuring system for steel

11,5 1/K 10 /K

Standard uncertainty from expansion coefficients of test part uαOBJ for steel

1,2 1/K 10 /K

Standard uncertainty from expansion coefficients of measuring system uαR for steel

1,2 1/K 10 /K

Maximum temperature (environment)

30° C

Value displayed by measuring system

64,505 mm

error limit from compensation for temperature difference

2,2 μm

Capability ratio limit measurement process QMP_max

30%

-6

-6

-6

In order to determine the standard uncertainties from repeatability on standards and from measurement bias, 10 repeated measurements on each of 3 reference standards were performed in an experiment.

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The information about the measuring system and the measured quantity values gained in the experiment lead to the following uncertainty budget and overview of results.

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Due to a percentage resolution %RE of 0,2% and a capability ratio QMS of 12,69%, the measuring system capability of the multiple-point measuring instrument is established. Secondly, the entire measurement process is observed. In an experiment, the influence factors of repeatability on standards, reproducibility of places of measurement and of their interactions are determined. Moreover, the temperature influence after the calculation without correcting linear expansion and a residual uncertainty from compensation for temperature difference are considered. In order to calculate the residual uncertainty from compensation for temperature difference, an individual experiment was conducted during a preliminary investigation (measured quantity value plotted on the temperature sequence/cooling curve is constant) and a error limit of 2,2 μm was determined. In the experiment for the measurement process, 2 repeated measurements were taken at every measuring point on each of 10 test parts. The recorded measured quantity values are evaluated using the method of ANOVA.

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The information about the measuring system and the measured quantity values gained in the experiment lead to the following uncertainty budget and overview of results.

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Due to a capability ratio QMP of 21,03% in case of a process capability ratio limit QMP_max of 30%, the measurement process capability of the multiplepoint measuring instrument is established.

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Annex F.6

Optimizing a Measurement Process

During an in-process inspection, the diameter of an engine shaft shall be measured. For this purpose, a qualified measuring system must be selected in order to evaluate the entire measurement process. A first review is based on a measuring system composed of a precision snap gauge, a mechanical dial gauge and a working standard. A general selection and evaluation of the measuring system / measurement process is based on the general data about the respective measurement component (mechanical dial gauge, precision snap gauge, working standard, etc.) rather than on specific individual data.

Engine shaft specifications Nominal dimension

8 mm

Upper deviation

+0,010 mm

Lower deviation

+0,001 mm

Upper specification limit U

8,010 mm

Lower specification limit L

8,001 mm

Roundness

0,003 mm

Information about mechanical dial gauge Resolution of the measuring system RE (1 digit = 0,0005 mm)

0,5 µm

Deviation range ftotal (MPE)

0,6 µm

Measuring interval

+/- 25 µm

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Information about precision snap gauge Parallelism (according to specification)

0,6 µm

Measuring force

3-10 N

Adjustment range

0 – 30 mm

Measuring span

2 mm

Measuring surfaces

D 8 mm

Information about working standard Reference value of standard

8,0005 mm

Calibration uncertainty UCAL

0,6 µm

Coverage factor kCAL

2

Temperature during calibration

20° C

Linearity uLIN

0

Before a measuring system can be applied for measurements, it must be set using a standard. The measuring system is set according to the calibrated actual value of the standard (working standard) which makes the system ready for use. In order to check this procedure, 25 repeated measurements on the standard are performed and the uncertainty from “repeatability” and “measurement bias” is determined.

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Remark:

Even if a measuring system was set using a reference standard, the limits of error of the dial gauge and the deviations of the precision snap gauge must be considered. Although the repeatability and systematic measurement error are known for this working point, they are unknown for measured quantity values lying around this working point. For values around the working point, the manufacturer of the measuring system only guarantees measurement results that do not exceed the specified limits of error (MPE). The same applies to the parallelism of the measuring surfaces and the setting using the standard. In this case, the deviations for the setting point (actual value of the working standard) are known, but they do not apply to lower or higher measured quantity values automatically.

A previous inspection confirmed that the deviations caused by temperature variations are negligible when the system is set once an hour because the materials have similar thermal expansion coefficients. The specifications, information and measured quantity values lead to the following uncertainty budget and overview of results.

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The overview of results shows that the capability of the measuring system with the mechanical dial gauge is not established due to a low resolution and a capability ratio QMS of 26,62% that is too high. Corrective action is taken by replacing the mechanical dial gauge by an incremental gauge with a lower MPE.

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Information about incremental gauge Resolution of the measuring system RE (1 digit = 0,0001 mm)

0,1 μm

MPE of incremental gauge

0,1 μm

Measuring interval 12 mm

12000 µm

In this case, the measuring system must also be set using a standard at first. The measuring system is set according to the calibrated actual value of the standard (working standard) which makes the system ready for use. In order to check this procedure, 25 repeated measurements on the standard are performed and the uncertainty from “repeatability” and “measurement bias” is determined.

The specifications, information and measured quantity values lead to the following uncertainty budget and overview of results for the measuring system with incremental gauge. Since the resolution is already included as an uncertainty component in the repeated measurements, it is not considered twice.

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The inspection of the measuring system with an incremental gauge shows that the resolution is sufficiently high, however, the capability ratio QMS exceeds the capability ratio limit QMS_max. As the uncertainty budget shows, capability cannot be established because of the influence of the parallelism of the precision snap gauge and the calibration uncertainty on the working standard. The next corrective action to be taken is to test a non-contact measuring instrument (laser micrometer). In this case, the measurement result is not affected by the main mechanical influence factor (parallelism of the precision

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snap gauge and calibration uncertainty on the working standard). The laser micrometer is calibrated by the manufacturer over the measuring interval and is ready for use immediately after it is switched on. Compared to the previous measuring systems, a laser micrometer need not be set using a working standard for the specified MPE range. Information about laser micrometer Resolution of the measuring system RE (1 digit = 0,0001 mm)

0,1 μm

Linearity deviation

0,2 µm

MPE of laser micrometer (calibrated at 20° C)

0,4 μm

Ambient temperature during the analysis of measured quantity values

26,5° C

In order to establish the measuring system capability of the laser micrometer under real conditions, 25 repeated measurements at the same measuring point of the standard is performed.

The measured quantity values and resolution of the measuring system lead to the following uncertainty budget.

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The uncertainty budget shows a high uncertainty from the measurement bias. This high influence is caused by the fact that all the recorded measured quantity values deviate from the reference quantity value of the standard uniformly because the reference quantity value of the standard was calibrated at 20° C. However, the laser micrometer measured the standard at an ambient temperature of 26,5° C. Due to the temperature variation, the reference standard is subject to linear expansion according to the formula:

Δl

ΔT α l

Expansion coefficient of reference standard: α (steel) = 11,5 +/-1 in 10−6 K−1 at 20° C Δ l = 6,5 * 11,5*10-6 * 8,0005 * = 0,598 µm = 0,6 µm. If the reference quantity value of the standard is reduced by 0,6 μm, the following uncertainty budget and the associated evaluations are obtained.

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Since a MPE is specified for the laser micrometer, the MPE is used for establishing measuring system capability in order to reduce the effort for the experiment. This leads to the following uncertainty budget and the associated evaluation of the measuring system.

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The overview of results shows that the measuring system of the laser micrometer meets the demands on the resolution %RE and the capability ratio QMS. The capability of the measuring system is established. In the next step, the measurement process is observed. In an experiment, 3 operators take 2 repeated measurements on each of 10 engine shafts.

This leads to an expanded uncertainty budget for the measurement process.

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Due to a capability ratio QMP of 13,48% in case of a process capability ratio limit QMP_max of 30%, a first review of the measurement process (without long-term analysis) establishes capability. The process can be used in production. In order to prove conformance or non-conformance, the form deviation (roundness) must be considered as a further influence factor affecting the test part. The following example is based on the information from a drawing where the maximum permissible measurement error amounts to 0,003 mm. Remark:

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Since a roundness figure always refers to a radius, it must be multiplied by a factor of 2 in order to analyze a diameter.

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The conformity evaluation shows that the permissible roundness results in a capability ratio QMP exceeding the process capability ratio limit QMP_max considerably. Thus, the capability of the entire measurement process including the maximum permissible measurement error is not established anymore. Corrective action can be taken by using a measurement method performing several measurements on the diameter of the engine shaft to be measured, By using laser micrometer, it is possible to record the mean, maximum and minimum value of a measurement e.g. in one revolution or in several measurements on the diameter. This method helps to reduce the uncertainty from form deviations considerably because the maximum and minimum diameters are actually measured. Thus, the customer is guaranteed that both diameters stay within the limits in the context of measurement uncertainty.

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The uncertainty from the minimum and maximum diameter of the manual measurement method was determined experimentally and amounts to R = 0,6 µm. Since the diameter was only measured at one measuring point, an additional uncertainty should be expected. An actual form deviation with a error limit of 0,9 µm is assumed. This leads to the following results.

Due to a capability ratio QMP of 26,74% in case of a process capability ratio limit QMP_max of 30%, the measurement process capability for production (without long-term analysis) is established. For further optimizing the measurement process, the manual measurement method for determining the form deviation was changed to an automated method. This leads to a error limit of 0,6 μm that is associated with the actu-

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al form deviation. The stability was observed in a long-term analysis and includes a error limit of 0,35 μm. This leads to the following uncertainty budget and overview of results.

Due to a capability ratio QMP of 22,34% in case of a process capability ratio limit QMP_max of 30%, the measurement process capability for production is established.

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Annex F.7 Compensation for Temperature Difference Calculating the standard uncertainty uT without correction of different linear expansions The nominal diameter of 85 mm shall be measured on a test part made of aluminium, however, without making any major compensation for temperature difference. A setting ring gauge made of steel is used for a comparison measurement. Temperatures of up to 30°C can occur at the workstation. There are not any precise information about the expansion coefficients of the test part and setting ring gauge available. Information about temperature influences Nominal dimension

85,00 mm

Length of the standard at 20° C (Ø setting ring gauge) yR

85,002 mm

Maximum temperature tMAX

30° C

Expansion coefficient of test part αOBJ

0,000024 1/K

Expansion coefficient of standard αR

0,0000115 1/K

Standard uncertainty from thermal expansion coefficient of test part uαOBJ

10% of αOBJ

Standard uncertainty from thermal expansion coefficient of standard uαR

10% of αR

According to these specifications, the measurement error is calculated by the formula B.6

∆y = 85,002 ⋅ (30 − 20 ) ⋅ (0,000024 − 0,0000115 ) = 0,0106 mm . Because of uncertain expansion coefficients, the uncertainty from other influence components in case of a temperature deviation of 10° C from the reference temperature of 10° C is calculated by formula B.5.

uREST = 85,002 ⋅ 102 ⋅ 0,000001152 + 102 ⋅ 0,00000242 = 0,0023 mm.

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According to formula B.7, these results lead to the error limit of

a = 0,0106 + 2 ⋅ 0,0023 = 0,0152 mm and, according to formula B.8, to a standard uncertainty from temperature of

uT =

0,0152 3

= 0,0088 mm .

In this case (assuming that uα = 0,1⋅ αOBJ ;R ), the standard uncertainty OBJ ;R can also be determined with the help of Table B.2. Using the value uT = 10,3 µm per 100 mm from the table, the following result is obtained (aside from little rounding differences):

uT = 10,3 ⋅

85,002 = 8,76 µm. 100

Calculating the standard uncertainty uT with correction of different linear expansions The uncertainty budget shows that the uncertainty component displayed above is too high. Therefore, the measurement results are corrected in order to reduce the uncertainty components to an acceptable level. In order to record the temperatures occurring during the measurement, a temperature measuring device is used that, according to manufacturer specifications, does not exceed a maximum deviation of ± 0,5° C. In case of the test part temperature of 28,2° C and the setting ring gauge temperature of 26,7° C, a difference of d = +0,014 mm was measured. This leads to a measured quantity value of Ø 85,016 mm. This measured value is corrected according to formula B.3.

y korr =

85,002 ⋅ (1+ 0,0000115 ⋅ (26,7 − 20)) + 0,014 1 + 0,000024 ⋅ (28,2 − 20)

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= 85,0058 mm.

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Since the standard uncertainty from the temperature measurement amounts to u ∆T = 0,5 3 = 0,2887 , a residual uncertainty remains according to OBJ ; R B.5 that represents the standard uncertainty from temperature that is now considerably smaller.

uT = 85,002 ⋅

6,72 ⋅ 0,000001152 + 8,22 ⋅ 0,00000242 + +0,00001152 ⋅ 0,28872 + 0,0000242 ⋅ 0,28872

= 0,0019 mm.

Conclusion:

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The advantage of avoiding complicated temperature measurements and compensations in case of high maximum temperatures is always gained on account of a relatively high (often too high) uncertainty component caused by temperature influences. For this reason, in most cases, the more time-consuming method is required, i.e. the temperatures occurring during the measurement must be determined and taken into account. Where possible, the application of modern, computer-based measuring instruments should be considered in test planning. These instruments perform and make most of the measurements and calculations that the users otherwise have to do themselves.

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Annex F.8 Inspection by Attribute without Critical Values A procedure for the visual inspection of semi-finished surfaces on die casting components requires that the capability of the measurement process is established and documented. 2 operators perform 3 repeated measurements on each of 40 semi-finished surfaces. The results of both operators are plotted on a matrix and compared. Then they are checked for symmetry using the Bowker test. The 95% quantile of the χ² distribution with 3 degrees of freedom is used as a critical value. The test results are displayed in the matrix below. Their evaluation is shown in the overview of results. Operator B

No. of repetitions

Result

Result

Result

mixed Result

Result Operator A mixed Result

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Thus, the test result exceeds the critical value of the 95% level of confidence, i.e. there is no symmetrical relation between the test results of the two operators. The procedure of the visual inspection is not suitable for semi-finished surfaces. In order to improve the visual inspection, a new catalogue of boundary samples is introduced and both operators repeat the entire test. This leads to the following matrix and overview of results. Operator B

No. of repetitions

Result

Result

Result

mixed Result

Operator A

Result mixed Result

² = 2,20 does not exceed the critical value of 7,81. A symmetrical relation between the test results of the two operators is proved. The capability of the visual inspection including a new catalogue of boundary samples is established.

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Annex F.9

Inspection by Attribute with Reference Values

The measurement process capability should be established and documented for a measurement procedure with one characteristic that can only be measured by using gauges. Information about attribute measurement process Nominal value

3,600 mm

Upper specification limit U

3,638 mm

Lower specification limit L Measurement QATTR_max

process

3,562mm capability

ratio

limit

30%

The information above specifies the characteristic. Two operators shall perform 2 repeated measurements on each of 20 reference parts. These inspections provide the following unsorted and sorted test results.

Unsorted test results

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Sorted test results

The following statistical values are calculated from the test results. Last test with agreement on negative result First test with agreement on positive result Last test with agreement on positive result First test with agreement on negative result

3,663 3,621 3,583 3,555

Ranges of the upper and lower conformance zones dU = 3,663 – 3,621 = 0,042 dL = 3,583 – 3,555 = 0,028 Average range d = (dU + dL) / 2 = (0,042 + 0,028) / 2 = 0,035 Uncertainty range and capability ratio UATTR = d / 2 = 0,035 / 2 = 0,0175 QATTR = 2 UATTR / TOL 100% = 2 0,0175 / 0,076 100% = 46,05 %

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Due to a capability ratio QATTR of 46,05% in case of a capability ratio limit QATTR_max of 30%, the capability of the measurement procedure using reference values is not established.

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11

Index of Formula Symbols Symbol MPE uAV

Term maximum permissible measurement error standard uncertainty from reproducibility of operator

uBI

standard uncertainty from measurement bias

uCAL

calibration standard uncertainty on a standard

uEV

standard uncertainty from maximum value of repeatability or resolution measuring system: max {uEVR, uRE} measurement process: max {uEVR, uEVO, uRE}

uEVO

standard uncertainty from repeatability on test parts

uEVR

standard uncertainty from repeatability on standards

uGV

standard uncertainty from reproducibility of measuring system

uIAi

standard uncertainty from interactions

uLIN

standard uncertainty from linearity

uMP

combined standard uncertainty on measurement process

uMS

combined standard uncertainty on measuring system

uMS_REST uOBJ

standard uncertainty from test part inhomogeneity

uRE

standard uncertainty from resolution of measuring system

uREST

standard uncertainty from other influence components not included in the analysis of the measurement process

uSTAB

standard uncertainty from stability of measuring system

uT

standard uncertainty from temperature

u(xi)

standard uncertainty

u(y)

combined standard uncertainty

UATTR

154

standard uncertainty from other influence components not included in the measuring system analysis

uncertainty range

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Symbol

Term

UMP

expanded measurement uncertainty (measurement process)

UMS

expanded measurement uncertainty (measuring system)

RE

resolution

Bi

bias

QMS

capability ratio (measuring system)

QMP

capability ratio (measurement process)

QMS_max

capability ratio limit (measuring system)

QMP_max

capability ratio limit (measurement process)

TOL

tolerance

TOLMIN-UMP minimum permissible tolerance of measurement process TOLMIN-UMS minimum permissible tolerance of measuring system k

coverage factor

a

variation limit

b

distribution factor

U

1)

upper specification limit U (specification limit that defines the upper limiting value)

L

1)

lower specification limit L (specification limit that defines the lower limiting value)

P 1)

test result, characteristic value

The GUM [22] or ISO 14253 [13] uses the formula symbol U for the expanded measurement uncertainty. However, new standards, such as ISO 3534-2 [9] refer to the upper specification limit as U. In order to avoid confusions in this document, the expanded measurement uncertainty is referred to as UMS where the measuring system is concerned and UMP when it is about the measurement process.

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Symbol Term UCL

upper control limit

LCL

lower control limit

Cg

capability index of measuring system

Cgk

minimum capability index of measuring system

Cp;real

real process capability index

sg

standard deviation

xm

reference quantity value of the standard

xmu

reference quantity value of the standard at the upper specification limit

xmm

reference quantity value of the standard in the centre of the specification

xml

reference quantity value of the standard at the lower specification limit

Cp;obs T ∆TOBJ

observed process capability index temperature temperature deviation of test part from 20° C

∆TR

temperature deviation of scale or standard from 20° C

αOBJ

thermal expansion coefficient of test part

αR

thermal expansion coefficient of scale or standard

yR

length of standard at a reference temperature of 20° C

ycorr

corrected measured quantity value

d

temperature difference between test part and standard

yi

measured quantity value

Y

measurement result (measured quantity value yi including the expanded measurement uncertainty UMP)

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Symbol Term N

number of standards (n = 1, ..., N)

K

number of repeated measurements (k = 1, ..., K) per standard

KB

class width

σ²

variance

xn

conventional true value for the n-th standard

yn

measured quantity value of the n-th standard

ynk

k-th of K measurements on the n-th of N standards

x

arithmetic mean of all conventional true values

y

arithmetic mean of all measured quantity values

εnk

deviation of the measured quantity value of the k-th of K measurements on the nth of N standards from its expected value

enk

residuals of the k-th of K measurements on the n-th of N standards

β0

y-intercept

βˆ 0

estimated y-intercept

β1

slope of the regression function

βˆ1

estimated slope of the regression function

1-α

z1-α / 2 f

t f ,1-α / 2

level of confidence quantile of standard normal distribution number of degrees of freedom quantile of Student t-distribution with f degrees of freedom

SS

sum of squares

MS

mean square

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12

Bibliography [1]

A.I.A.G. – Chrysler Corp., Ford Motor Co., General Motors Corp. th Measurement Systems Analysis, Reference Manual, 4 edition. Michigan, USA, 2010.

[2]

Deutscher Kalibrierdienst DKD-3: Angabe der Messunsicherheit bei Kalibrierungen. DKD bei der PTB, Braunschweig, 2002.

[3]

Deutscher Kalibrierdienst DKD-4: Rückführung von Mess- und Prüfmitteln auf nationale Normale. DKD bei der PTB, Braunschweig, 1998.

[4]

Dietrich, E. / Schulze, A. Measurement Process Qualification: Gage Acceptance and Measth urement Uncertainty According to Current Standards, 3 completely revised edition. Carl Hanser Verlag, Munich, 2011.

[5]

DIN - Deutsches Institut für Normung DIN 1319-1: Grundlagen der Messtechnik – Teil 1: Grundbegriffe. Beuth Verlag, Berlin, 1995.

[6]

DIN - Deutsches Institut für Normung DIN 1319-2: Grundlagen der Messtechnik – Teil 2: Begriffe für die Anwendung von Messgeräten. Beuth Verlag, Berlin, 1996.

[7]

DIN - Deutsches Institut für Normung DIN 1319-3: Grundlagen der Messtechnik – Teil 3: Auswertung von Messungen einer einzelnen Messgröße, Messunsicherheit. Beuth Verlag, Berlin, 1996.

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[8]

DIN - Deutsches Institut für Normung DIN 55319-3: Statistische Verfahren - Teil 3: Qualitätsfähigkeitskenngrößen zur Beurteilung von Messprozessen bei multivariat normalverteilten Messergebnissen. Beuth Verlag, Berlin, 2007.

[9]

DIN - Deutsches Institut für Normung ISO 3534-1 to 3534-3: Statistics – Vocabulary and symbols. Beuth Verlag, Berlin, 2006.

[10] DIN - Deutsches Institut für Normung ISO 9000:2005: Quality management systems – Fundamentals and Vocabulary. Beuth Verlag, Berlin, 2005. [11] DIN - Deutsches Institut für Normung DIN EN ISO 9001:2008: Qualitätsmanagementsysteme - Anforderungen. Beuth Verlag, Berlin, 2008. [12] DIN - Deutsches Institut für Normung EN ISO 10012:2003 Measurement management systems – Requirements for measurement processes and measuring equipment. Beuth Verlag, Berlin, 2004. [13] DIN - Deutsches Institut für Normung ISO/TS 14253-1: Geometrical product specifications (GPS). Inspection by measurement of workpieces and measuring equipment. Part 1: Decision rules for proving conformance or non-conformance with specifications. Beuth Verlag, Berlin, 1999. [14] DIN - Deutsches Institut für Normung Supplemetary sheet to ISO/TS 14253-1: Inspection by measurement of workpieces and measuring equipment. Guide to the estimation of uncertainty in GPS measurement, in calibration of measuring equipment and in production verification. Beuth Verlag, Berlin, 1999.

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[15] DIN - Deutsches Institut für Normung ISO/TR 14253-2: Geometrical product specifications (GPS) – Inspection by measurement of workpieces and measuring equipment. Part 2: Guide to the estimation of uncertainty in GPS measurement, in calibration of measuring equipment and in product verification. International Organization for Standardization, Geneva, 1998. [16] DIN - Deutsches Institut für Normung ISO/TS 15530-3:2009-07 Geometrical Product Specifications (GPS) – Coordinate measuring machines (CMM): Techniques for evaluation of the uncertainty of measurement - Part 3: Use of calibrated workpieces. Beuth Verlag, Berlin, 2000. [17] DIN - Deutsches Institut für Normung EN ISO/IEC 17000:2004 Conformity assessment – Vocabulary and general principles Beuth Verlag, Berlin, 2005. [18] DIN - Deutsches Institut für Normung EN ISO/IEC 17024:2003 Conformity assessment - General requirements for bodies operating certification of persons Beuth Verlag, Berlin, 2003. [19] DIN - Deutsches Institut für Normung EN ISO/IEC 17025:2005 General requirements for the competence of testing and calibration laboratories Beuth Verlag, Berlin, 2005.

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[20] DIN - Deutsches Institut für Normung DIN ISO 55350-12: Ausgabe: 1989-03 Begriffe der Qualitätssicherung und Statistik; Merkmalsbezogene Begriffe. Beuth Verlag, Berlin, 1989. [21] DIN - Deutsches Institut für Normung DIN ISO/IEC Guide 99:2007 International vocabulary of metrology (VIM). Beuth Verlag, Berlin, 2010. [22] DIN - Deutsches Institut für Normung ISO/IEC Guide 98-3 (2008): Guide to the expression of uncertainty in measurement (GUM:1995). International Organization for Standardization, Geneva, 2008. [23] DIN - Deutsches Institut für Normung ISO/TS 16949:2009-06 Vornorm: Qualitätsmanagementsysteme Besondere Anforderungen bei Anwendungen von ISO 9001:2008 für die Serien- und Ersatzteil-Produktion in der Automobilindustrie. Beuth Verlag, Berlin, 2009. [24] ISO – International Standard Organization ISO/WD 22514-7: Capability and performance – Part 7: Capability of Measurement Processes. Geneva, 2008. ®

[25] Q-DAS GmbH Leitfaden der Automobilindustrie zum „Fähigkeitsnachweis von Messsystemen“, Version 2.1. Weinheim, 2002.

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[26] VDA – Verband der Automobilindustrie VDA Band 6: Teil 1 - QM - Systemaudit - Grundlage DIN EN ISO 9001 und DIN EN ISO 9004-1. VDA, Frankfurt, 2003. [27] VDI/VDE/DGQ Richtlinie VDI/VDE/DGQ 2617, Blatt 7: Genauigkeit von Koordinatenmessgeräten - Kenngrößen und deren Prüfung - Ermittlung der Unsicherheit von Messungen auf Koordinatenmessgeräten durch Simulation. Beuth Verlag, Berlin, 2008. [28] VDI/VDE/DGQ Richtlinie VDI/VDE/DGQ 2618, Blatt 9.1: Prüfmittelüberwachung - Prüfanweisung für Messschieber für Außen-, Innen- und Tiefenmaße. Beuth Verlag, Berlin, 2006.

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13

Index coverage factor · 14, 37 coverage probability · 55 C-value · 47

A absolute measurement · 99 action limits · 71, 78 adjustment · 18 ANOVA · 16 ANOVA tables · 94 ARM · 35 attribute · 85 averaging · 105

D definitions · 13 D-optimum design · 35 D-optimum experimental design · 114

E

B

environment · 27, 48 evaluation method · 28 expanded · 68 expanded measurement uncertainty · 15, 29, 37, 38

bias · 16, 50 bibliography · 158

C calibration · 18 calibration uncertainty · 50, 52, 74 capability · 26, 43, 46 capability analysis · 49, 64 capability of measuring system · 56 capability of the measuring system · 20 capability of the measuring system · 29 capability ratios · 40, 68 characteristic · 16 classification · 81 CMM · 117 combined measurement uncertainty · 38 comparison measurement · 99 conformance zone · 23 conformity · 15 conformity assessment · 15, 29 control chart · 22, 71 control limit · 72 conventional true value · 17 correction · 60, 99, 102

F form deviation · 78 formula symbols · 154

G guidelines · 10 GUM · 10, 13

I influences · 26, 75 inspection by attribute · 151 inspection by attributes gauging · 17 inspection by variables measuring · 17 instrumental drift · 31 interactions · 69, 112

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intermediate measurement precision · 16 intermediate precision · 16

K k factors · 107

N

L

non-conformance zone · 24 not capable · 47

lack-of-fit · 61, 62 error limit · 37 limits · 42 linear expansion · 99 linearity · 53 linearity analysis · 60 linearity deviation · 50, 52, 76 long-term analysis · 32

M

O obsered process capability index · 46 ongoing review · 29, 71, 92 operator · 15, 28, 48, 65, 68 operator influence · 76 outlier · 31, 62

P

man · 28 material measures · 48, 58, 59 maximum permissible measurement error · 20 measured quantity value · 16 measurement error systematic · 30, 50, 52, 53, 75 measurement method · 28 measurement procedure · 28 measurement process · 20, 49, 64 measurement process capability · 20, 29 measurement process models · 79 measurement repeatability · 16, 50 measurement result · 16 measurement software · 82 measurement stability · 48, 71 measurement standard · 17, 27 measurement uncertainty · 11, 13 measuring equipment · 19, 27, 48 measuring instrument · 19 measuring system · 19, 27, 50

164

method of moments · 35 metrological traceability · 18 minimum tolerance · 44, 68 mounting device · 28 MPE · 43 MSA · 16

place of measurement · 65, 77 pooling · 69, 97

R random measurement errors · 30 reference quantity values · 86, 88 reference standard · 13, 27 reference temperature · 100 reference values · 149, 151 regression function · 50, 60, 72, 93 regression line · 60 repeatability · 16, 52, 53, 75 repeated measurements · 52, 54, 60 reproducibility · 32, 76 residual · 63 residual standard deviation · 60, 61 residuals · 93 resolution · 19, 50, 52, 53, 74

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S setting · 19 setting a working point · 108 small geometric elements · 81 small tolerances · 81 special measurement processes · 81 stability · 20, 48 stability of the measuring instrument · 20 standard · 52 standard measurement uncertainty · 14 combined · 14 standard normal distribution · 63 standard uncertainty · 14 combined · 14 standards · 10

T temperature · 48, 78 temperture influences · 98 test characteristic · 15 test part · 28, 65, 78 test parts · 48 testing · 15 thermal expansion coefficient · 99 time · 65, 78 tolerance · 21

true value · 17 Type 1 study · 54, 56, 75 Type 2 study · 65, 76 Type 3 study · 77 Type A evaluation · 34 ANOVA · 34 standard deviation · 34 Type B evaluation · 33, 36

U uncertainty budget · 14, 45 uncertainty component · 14 uncertainty components · 65 uncertainty range · 25, 85, 88

V validation · 21, 82 value of the characteristic · 16 verification · 21 vibrations · 48 VIM · 13

W working standard · 17

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Quality Management in the Automotive Industry The current versions of the published VDA volumes about the quality management in the automotive industry (QAI) are found online under http://www.vda-qmc.de/en/. Our publications can be ordered from our website directly.

Reference: Verband der Automobilindustrie e.V. (VDA) Qualitäts Management Center (QMC) Behrenstraße 35, 10117 Berlin, Germany Telephone +49 (0) 30 897842 - 235, Telefax +49 (0) 30 897842 - 605 E-Mail: [email protected], Internet: www.vda-qmc.de

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