Multi-response optimization in industrial experiments using Taguchi's quality loss function and principal component analysis

Jiju Antony

    Research output: Contribution to journalArticlepeer-review

    203 Scopus citations

    Abstract

    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.

    Original languageBritish English
    Pages (from-to)3-8
    Number of pages6
    JournalQuality and Reliability Engineering International
    Volume16
    Issue number1
    DOIs
    StatePublished - Jan 2000

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