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Response surface methodology considering Poisson and Weibull regression models: a case study

Abstract

This paper presents new statistical modeling under a Bayesian approach to analyze response surfaces in industrial experiments where the responses are given by both count data and lifetime data. As a motivation and application of the proposed methodology, we analyzed a data set related to manufacturing of 304 stainless steel components of medical tools in a metal industry in Ribeirão Preto, São Paulo State, Brazil. To manufacture batches of these components of medical tools, a cutting tool of the manufacture machine is used until there is failure of this tool that should be replaced by a new one. A first goal of this industrial sector is to identify possible factors that affect the number of manufactured components of medical tools until cutting tool failure and the manufacturing time of each unit. Multivariate linear regression models for discrete and lifetime data under a Bayesian approach were used for this purpose. The final goal of this study was to determine the optimal levels of the factors which maximize the number of manufactured components of medical tools in each batch and minimize the manufacturing time using response surface methodology.

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Correspondence to Roberto Molina de Souza.

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Achcar, J.A., Faria, R.F. & de Souza, R. Response surface methodology considering Poisson and Weibull regression models: a case study. Int J Adv Manuf Technol 77, 1867–1879 (2015). https://doi.org/10.1007/s00170-014-6584-y

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Keywords

  • Poisson regression model
  • Weibull regression model
  • Response surface analysis
  • Bayesian methods