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Capability Testing Based on C pm with Multiple Samples
Author(s) -
Wu ChienWei,
Pearn W. L.
Publication year - 2005
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.605
Subject(s) - process capability , process capability index , bayesian probability , reliability engineering , quality assurance , computer science , posterior probability , process (computing) , sample (material) , data mining , engineering , artificial intelligence , work in process , operations management , operating system , chemistry , external quality assessment , chromatography
Numerous process capability indices have been proposed in the manufacturing industry to provide unitless measures on process performance, which are effective tools for quality improvement and assurance. Most existing methods for capability testing are based on the distribution frequency approaches. Recently, Bayesian approaches have been proposed for testing capability indices C p and C pm but restricted to cases with one single sample. In this paper, we consider estimating and testing capability index C pm based on multiple samples. We propose accordingly a Bayesian procedure for testing C pm . Based on the Bayesian procedure, we develop a simple but practical procedure for practitioners to use in determining whether their manufacturing processes are capable of reproducing products satisfying the preset capability requirement. A process is capable if all the points in the credible interval are greater than the pre‐specified capability level. To make the proposed Bayesian approach practical for in‐plant applications, we tabulate the minimum values of $C^{\ast} (p)$ for which the posterior probability p reaches various desirable confidence levels. Copyright © 2004 John Wiley & Sons, Ltd.