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Journal of Intelligent Manufacturing

, Volume 29, Issue 8, pp 1683–1693 | Cite as

A comparison of machine learning methods for cutting parameters prediction in high speed turning process

  • Zoran Jurkovic
  • Goran Cukor
  • Miran Brezocnik
  • Tomislav Brajkovic
Article

Abstract

Support vector machines are arguably one of the most successful methods for data classification, but when using them in regression problems, literature suggests that their performance is no longer state-of-the-art. This paper compares performances of three machine learning methods for the prediction of independent output cutting parameters in a high speed turning process. Observed parameters were the surface roughness (Ra), cutting force \((F_{c})\), and tool lifetime (T). For the modelling, support vector regression (SVR), polynomial (quadratic) regression, and artificial neural network (ANN) were used. In this research, polynomial regression has outperformed SVR and ANN in the case of \(F_{c}\) and Ra prediction, while ANN had the best performance in the case of T, but also the worst performance in the case of \(F_{c}\) and Ra. The study has also shown that in SVR, the polynomial kernel has outperformed linear kernel and RBF kernel. In addition, there was no significant difference in performance between SVR and polynomial regression for prediction of all three output machining parameters.

Keywords

Turning Roughness Cutting force Tool life ANN SVR 

Notes

Acknowledgments

This work was supported and funded by the University of Rijeka, Croatia, (OJ 212, MT 137), and Ministry of Science, Education and Sport of the Republic of Croatia under bilateral cooperation with University of Maribor, Slovenia.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Zoran Jurkovic
    • 1
  • Goran Cukor
    • 1
  • Miran Brezocnik
    • 2
  • Tomislav Brajkovic
    • 1
  1. 1.Faculty of EngineeringUniversity of RijekaRijekaCroatia
  2. 2.Faculty of Mechanical EngineeringUniversity of MariborMariborSlovenia

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