Goran Cukor

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

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

Application of the CAD/CAM System in the Production of the Anatomic Insole

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IMPROVING THE SURFACE ROUGHNESS AT LONGITUDINAL TURNING USING THE DIFFERENT OPTIMIZATION METHODS

(Fc), 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

Testing of machinability of mould steel 40CrMnMo7 using genetic algorithm

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Genetic Algorithm Application in Optimization of Extrusion Forming Process

Fc and Ra prediction, while ANN had the best performance in the case of T, but also the worst performance in the case of