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

Rossella Berni (), Lorenzo Piattoli, Christine Michaela Anderson-Cook and Lu Lu
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Rossella Berni: Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze, https://www.disia.unifi.it
Lorenzo Piattoli: Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze, https://www.disia.unifi.it
Christine Michaela Anderson-Cook: Los Alamos, New Mexico (USA)
Lu Lu: Department of Mathematics and Statistics, University of South Florida, Florida (USA)

No 2021_17, Econometrics Working Papers Archive from Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti"

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.

Keywords: design of experiment; robust process oprimization; Pareto front approach (search for similar items in EconPapers)
Pages: 23 pages
Date: 2021-09
New Economics Papers: this item is included in nep-ore
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