Hostname: page-component-7c8c6479df-8mjnm Total loading time: 0 Render date: 2024-03-28T19:08:37.603Z Has data issue: false hasContentIssue false

An overview of capability evaluation of Measurement Systems and Gauge Repeatability and Reproducibility Studies

Published online by Cambridge University Press:  17 December 2010

Get access

Abstract

This study is an overview of the capability evaluation of measurement systems. Principally, the determination of capability of a measurement system is an important aspect of quality and process improvement initiatives. In practice, various methods are developed and used for determining measurement system capability. As a measurement tool, potential effectiveness of gauge should be considered and significant factors should be identified for that purpose. A set of procedures referred to as Measurement Systems Capability Studies are conducted for assessing capability of gauge, isolating sources of variability in the system, evaluating how much of total observed variability is due to gauge, and investigating two components of measurement error: repeatability and reproducibility of gauge. Gauge Repeatability & Reproducibility (Gauge R&R) Study tries to estimate repeatability and reproducibility components of measurement system variation with primary objective of assessing whether gauge is suitable for intended application or not. Measurement System Analysis (MSA) is a collection of statistical methods, which includes Gauge R&R Study, for analysis of measurement system capability. In this study, detailed literature review of MSA, Gauge R&R and Measurement Systems Capability Studies, and general discussion of misclassification probabilities that give useful, reliable information about measurement systems performance would be provided.

Type
Research Article
Copyright
© EDP Sciences 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Références

D.C. Montgomery, Introduction to Statistical Quality Control, 6th edn. (John Wiley & Sons, New York, 2009)
Dick Smith, Rathel R., McCrary, S.W., R. Neal Callahan, Gauge repeatability and reproducibility studies and measurement system analysis: A multimethod exploration of the state of practice, J. Ind. Technol. 23, 1-12 (2007) Google Scholar
R. Tabisz, The capability evaluating of industrial measurement systems, in XVII IMEKO World Congress June 22–27, Dubrovnik, Croatia, 2003
Little, T., 10 Requirements for Effective Process Control: A Case Study, Qual. Progress 34, 46-52 (2001) Google Scholar
Joiner, B.L., Gaudard, M.A., Variation, management, and W. Edwards Deming, Qual. Progress 23, 29-36 (1990) Google Scholar
Zappa, D., Deldossi, L., Misclassification rates, critical values and size of the design in measurement systems capability studies, Appl. Stoch. Mod. Bus. Ind. 25, 601-611 (2009) CrossRefGoogle Scholar
Automotive Industry Action Group (AIAG), Measurement systems analysis, 3rd edn. (Author, Southfield, MI, 2002)
Burdick, R.K., Borror, C.M., Montgomery, D.C., A review of methods for measurement systems capability analysis, J. Qual. Technol. 35, 342-354 (2003) Google Scholar
D.H. Ballard, D.W. McCormack Jr., T.L. Moore, J. Prins, P.A. Tobias, M. Pore, A Comparison of Gauge Study Practices, in ASA Proc. of the Section on Quality and Productivity, 1997, pp. 31–36
Woodall, W.H., Borror, C.M., Some relationships between Gage R&R Criteria, Qual. Reliab. Eng. Int. 24, 99-106 (2008) CrossRefGoogle Scholar
M.J. Harry, J.R. Lawson, Six sigma producibility analysis and process characterization (Addison-Wesley, New York, 1992)
Hoffa, D.W., Laux, C., Gauge R&R: An effective methodology for determining the adequacy of a new measurement system for micron-level metrology, J. Ind. Technol. 23, 1-9 (2007) Google Scholar
D.H. Besterfield, Quality Control, 7th edn. (Prentice Hall, Englewood Cliffs, New Jersey, 2004)
Lupan, R., Bacivarof, I.C., A Relationship between Six Sigma and ISO 9000:2000, Qual. Eng. 17, 719-725 (2005) CrossRefGoogle Scholar
Antony, J., Kumar, M., Tiwari, M.K., An application of six sigma methodology to reduce the engine-overheating problem in an automotive company, J. Eng. Manuf. B8 14, 633-646 (2005) CrossRefGoogle Scholar
Automotive Industry Action Group (AIAG), Statistical Process Control, 2nd edn. (Author, Southfield, MI, 2005)
Mader, D.P., Prins, J., Lampe, R.E., The Economic Impact of Measurement Error, Qual. Eng. 11, 563-574 (1999)CrossRefGoogle Scholar