Manufacturing process performance evaluation for fuzzy data based on loss-based capability index

Abstract

Process capability indices have been introduced for measuring process reproduction capability of a manufacturing industry. The loss-based capability index C pm takes into account the degree of process targeting (centering), which essentially measures the process performance on the basis of average process loss. Generally, the underlying manufacturing process data are obtained from the output responses of continuous quantities, which are always assumed to be real numbers. However, in a practical situation, the data collected by output process measurements are often imprecise (fuzzy data). We propose a constructive methodology for obtaining the fuzzy estimate of C pm using fuzzy data, which is based on ''resolution identity'' in fuzzy sets theory. We propose four decision rules to judge the process condition by simultaneously introducing randomness and fuzziness. Finally, we present a sequence of testing steps for assessing manufacturing process performance using the critical value of C pm with fuzzy data and can be easily implemented in real situations.

A Multidimensional Data Flow Driven-Based Quality Fluctuation Evaluation for Manufacturing Process

“…In the field of process performance evaluation, there have been remarkable research works on quality stability evaluation of process fluctuation [21][22][23]. Daniel et al [24] proposed an approximate entropy method to identify chatter instabilities in milling process.…”

Bayesian Test for Lifetime Performance Index of Exponential Distribution under Symmetric Entropy Loss Function

“…Process capability indices (PCIs) have been introduced for measuring process reproduction capability of a manufacturing industry [1]. They are important in the manufacturing industry to measure process potential and performance [2].…”

Confidence based quality evaluation for total manufacturing process using comprehensive process capability

“…Junwen Wang [3] presented an analytical method based on the Markov chain model to evaluate the quality performance of flexible manufacturing systems with batch operations; In order to establish performance measures for evaluating the capability of a multivariate manufacturing process, JehNan Pana [4] developed two novel capability indices based on the correlation among multiple quality characteristics; Ihsan Kaya [5] developed the fuzzy formulations of the process capability indices based on the fuzzy set theory; Ming-Hung Shu [6] proposed a constructive methodology for obtaining the fuzzy estimate of PCIs using fuzzy data, which is based on "resolution identity" in fuzzy sets theory; Alan M. Polansky [7] proposed a capability assessing approach of a manufacturing process using nonparametric Bayesian density estimation, this framework relies on using mixtures of Dirichlet processes to elicit a prior on the multivariate distribution of the quality characteristic; Kai Gu [8]put forward a yield-based capability index for evaluating the performance of multivariate manufacturing process, which could establish performance measures for evaluating the capability of a multivariate manufacturing process. In order to metric the impact of the sample size and sampling scheme on the index estimation, there are a few of studies regarding the confidence intervals estimation of PCIs.…”