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Split-plot designs and multi-response process optimization: a comparison between two approaches

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Abstract

Nowadays split-plot designs play a crucial role in the technological field, both for their flexibility when applying a robust design approach and in relation to the modelling step, by considering Mixed Response Surface models and/or the class of Generalized Linear Mixed Models-GLMMs. In this paper, a split-plot design is studied in a process optimization scenario involving several response variables, e.g., a multi-response situation, in which a comparison between two optimization methods is performed. More precisely, by considering a real case study related to the improvement of a measurement process of a Numerical-Control machine (N/C machine) to measure dental implants, the optimization is carried out with the Pareto front approach and then compared with other analytical methods also used to optimize. The final discussion considers the advantages and disadvantages (of application) for both methods.

Suggested Citation

  • Rossella Berni & Lorenzo Piattoli & Christine Michaela Anderson-Cook & Lu Lu, 2021. "Split-plot designs and multi-response process optimization: a comparison between two approaches," Econometrics Working Papers Archive 2021_17, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    • Handle: RePEc:fir:econom:wp2021_17
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    File URL: https://labdisia.disia.unifi.it/wp_disia/2021/wp_disia_2021_17.pdf
    File Function: First version, 2021-09
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    References listed on IDEAS

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    1. J. A. Nelder & Y. Lee, 1991. "Generalized linear models for the analysis of taguchi‐type experiments," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 7(1), pages 107-120, March.
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    Keywords

    design of experiment; robust process oprimization; Pareto front approach;
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