Process Capability Indices

• Main Authors: K.S.Chen, 陳坤盛
• Other Authors: W.L.Pearn
• Format: Others
• Language: en_US
• Published: 1995
• Online Access: http://ndltd.ncl.edu.tw/handle/75435054946035602254

博士 === 國立交通大學 === 工業工程研究所 === 84 === Process capability indices (PCIs), whose initial purpose is to provide a numerical measure on whether a production process is capable of producing items within the specification limits preset by the designer. In chapter 1, we review several existing process capability indices with symmetric tolerances. In chapter 2, we review several existing process capability indices and proposed a new class of capability indices to handle processes with asymmetric tolerances. The proposed new indices are compared with the existing PCIs in terms of process yield, process centering, and other process characteristics. The results indicated that the new indices are superior to the existing capability indices. In chapter 3, we investigate the statistical properties of the estimators of the several existing process capability indices with symmetric tolerances. In addition, we considered a new (Bayesian-like) estimator Cpk to relax Bissell''s assumption on the process mean. It can be showed that by adding a well-known correction factor bf to the new estimator, we obtained an unbiased estimator of Cpk whose standard deviation is smaller than those given in Bissell (1990) and Kotz, Pearn and Johnson (1993). The variability reduction of the estimator provides a greater reliability in current practices of using Cpk to monitor process quality. In chapter 4, we investigate the statistical properties of the natural estimators of the new class of capability indices. In chapter 5, we first investigated Clements'' method for calculating the estimators of the four capability indices, Cp, Cpk, Cpm, and Cpmk for non-normal Pearsonian populations. Then, we considered a new estimating method to calculate estimators of the four capability indices for non-normal Pearsonian populations. The analysis showed that the estimators calculated from the proposed new method can differentiate on-target processes from off-target processes better than those obtained by applying Clements''method.