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

Zoran Jurkovic (), Goran Cukor (), Miran Brezocnik () and Tomislav Brajkovic ()
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Zoran Jurkovic: University of Rijeka
Goran Cukor: University of Rijeka
Miran Brezocnik: University of Maribor
Tomislav Brajkovic: University of Rijeka

Journal of Intelligent Manufacturing, 2018, vol. 29, issue 8, No 1, 1683-1693

Abstract: 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 $$(F_{c})$$ ( 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 $$F_{c}$$ 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 $$F_{c}$$ 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 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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DOI: 10.1007/s10845-016-1206-1

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