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Modelling Variation in Industrial Experiments

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  • J. Engel

Abstract

For off‐line quality control, the Taguchi method now receives much attention in industry. It effectively combines engineering knowledge with the power of the design of experiments, to minimize variability at the design stage of products and processes and to set the process level at the target value. The paper discusses the analysis of Taguchi experiments and proposes a simple and flexible model for the mean and variation of the data, as well as a model parameter estimation method that is based on sound statistical principles. Data from an industrial experiment are included for illustration.

Suggested Citation

  • J. Engel, 1992. "Modelling Variation in Industrial Experiments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(3), pages 579-593, November.
    • Handle: RePEc:bla:jorssc:v:41:y:1992:i:3:p:579-593
    DOI: 10.2307/2348091
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    Cited by:

    1. Erdbrügge, Martina & Kunert, Joachim, 2000. "A simulation study on the choice of transformations in Taguchi experiments," Technical Reports 2000,49, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. Kunert, Joachim & Auer, Corinna & Erdbrügge, Martina & Göbel, Roland, 2003. "An experiment to compare the combined array and the product array for robust parameter design," Technical Reports 2003,13, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

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