Summary: | 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.
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