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Impact of stochastic industrial variables on the cost optimization of AISI 52100 hardened-steel turning process

  • Alexandre Fonseca Torres
  • Fabrício Alves de AlmeidaEmail author
  • Anderson Paulo de Paiva
  • João Roberto Ferreira
  • Pedro Paulo Balestrassi
  • Paulo Henrique da Silva Campos
ORIGINAL ARTICLE
  • 41 Downloads

Abstract

An optimization problem of the AISI 52100 hard-steel turning process is examined. A new approach is presented in which not only the machine parameters (cutting speed, feed rate, and depth of cut) but also the stochastic industrial variables of setup time, insert changing time, batch size, machine and labor costs, tool holder price, tool holder life, and insert price are considered. By representing each of these variables by a given probability distribution, the goal was to analyze their impact on the total process cost per piece (Kp). Experiments were carried out following a central composite design to model tool life (T), average surface roughness (Ra), and peak-to-valley surface roughness (Rt) using a response surface methodology. Then, stochastic programming was used to model Kp’s expected value and standard deviation. The approach to the optimization problem aimed to maximize the probability for the cost to be less than a target value, subject to the experimental space and to maximum values of both Ra and Rt. The results were optimal values for the cutting conditions that provide a suitable confidence interval for Kp. The most-significant industrial variables on Kp were ranked. In addition, it was found that, in the addressed case, cutting conditions for maximum tool life actually increase Kp.

Keywords

Stochastic programming Hardened-steel turning Process cost optimization 

Notes

Acknowledgments

The authors would like to thank FAPEMIG, FUPAI, CNPq, and CAPES for supporting this research.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Alexandre Fonseca Torres
    • 1
  • Fabrício Alves de Almeida
    • 1
    Email author
  • Anderson Paulo de Paiva
    • 1
  • João Roberto Ferreira
    • 1
  • Pedro Paulo Balestrassi
    • 1
  • Paulo Henrique da Silva Campos
    • 1
  1. 1.Institute of Industrial EngineeringFederal University of ItajubáItajubáBrazil

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