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Title of Journal: J Intell Manuf

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Abbravation: Journal of Intelligent Manufacturing

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Springer US

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DOI

10.1016/0021-9797(87)90133-0

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ISSN

1572-8145

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A comparison of machine learning methods for cutti

Authors: Zoran Jurkovic Goran Cukor Miran Brezocnik Tomislav Brajkovic
Publish Date: 2016/02/22
Volume: 29, Issue: 8, Pages: 1683-1693
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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 stateoftheart 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


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