A Confidence Interval Estimation Method for Process Capability Index with Bias

Article Preview

Abstract:

Process capability index (PCI) has been widely applied in manufacturing industry as an effective management tool for quality evaluation and improvement, whose calculation in most existing research work is premised on the assumption that there exists no bias. In this paper, the bias of gauge which exerts an effect on the calculation of PCI is indicated inevitable. The influence on PCI caused by the bias is analyzed by constructing a comparative ratio R between the empirical process capability index and the PCI. A confidence interval estimation method is proposed to solve the underestimation problem of PCI.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

622-626

Citation:

Online since:

October 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] S. Kotz, N.L. Johnson, Process Capability Indices. Chapman and Hall, London, (1993).

Google Scholar

[2] D.R. Bothe, Measuring Process Capability, McGraw-Hill, New York, (1997).

Google Scholar

[3] S. Kotz, C.R. Lovelace, Process Capability Indices in Theory and Practice, Arnold, London, (1998).

Google Scholar

[4] W.L. Pearn, P.C., Lin, Measuring process yield based on the capability index Cpm, International Journal of Advanced Manufacturing Technology. 24 (2004) 503-508.

DOI: 10.1007/s00170-003-1586-1

Google Scholar

[5] Chien-Weiwu, W.L. Pearn, Capability testing based on Cpm with multiple samples. Quality and Reliability Engineering International. 21 (2005) 29-42.

Google Scholar

[6] S. Bordignon, M. Scagliarint, Statistical analysis of process capability index with measurement errors. Quality and Reliability Engineering International. 18 (2002) 321-332.

DOI: 10.1002/qre.464

Google Scholar

[7] S. Bordignon, M. Scagliarini, Estimation of Cpm when measurement error is present. Quality and Reliability Engineering International. 22 (2006) 787-801.

DOI: 10.1002/qre.728

Google Scholar

[8] B. M. Hsu, M. H. Shu, W. L. Pearn, Measuring process capability based on Cpmk with gauge measurement errors. Quality and Reliability Engineering International. 23 (2007) 597-614.

DOI: 10.1002/qre.836

Google Scholar

[9] M. Perakis, E. Xekalaki, A new method for constructing confidence intervals for the index Cpm, Quality and Reliability Engineering International. 20 (2004) 651-665.

DOI: 10.1002/qre.574

Google Scholar

[10] D. Benton, K. Krishnamoorthy, Computing discrete mixtures of continuous distributions: noncentral chisquare, noncentralt and the distribution of the square of the sample multiple correlation Coefficient, Computational Statistics & Data Analysis. 43(2) (2003).

DOI: 10.1016/s0167-9473(02)00283-9

Google Scholar

[11] L.S. Zimmer, N.F. Hubble, Percentiles of the sampling distribution of Cpm, Quality Engineering. 10 (1997) 309-329.

Google Scholar