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Multi‐response optimization in industrial experiments using Taguchi's quality loss function and principal component analysis
Author(s) -
Antony Jiju
Publication year - 2000
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/(sici)1099-1638(200001/02)16:1<3::aid-qre276>3.0.co;2-w
Subject(s) - taguchi methods , principal component analysis , judgement , component (thermodynamics) , quality (philosophy) , multivariate statistics , engineering , design of experiments , principal (computer security) , reliability engineering , function (biology) , process (computing) , operations research , statistical process control , industrial engineering , computer science , statistics , machine learning , artificial intelligence , mathematics , philosophy , physics , epistemology , evolutionary biology , biology , thermodynamics , political science , law , operating system
Many industrial experiments based on Taguchi's parameter design (PD) methodology deal with the optimization of a single performance quality characteristic. Studies have shown that the optimal factor settings for one performance characteristic are not necessarily compatible with those of other performance characteristics. Multi‐response problems have received very little attention among industrial engineers and Taguchi practitioners. Many Taguchi practitioners have employed engineering judgement for determining the final optimal condition when several responses are to be optimized. However, this approach always brings some level of uncertainty to the decision‐making process and is very subjective in nature. In order to rectify this problem, the author proposes an alternative approach using a powerful multivariate statistical method called principal component analysis (PCA). The paper also presents a case study in order to demonstrate the potential of this approach. Copyright © 2000 John Wiley & Sons, Ltd.