Zoran Jurković

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

Cybernetization of Industrial Product-Service Systems in Network Environment

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Učinkoviti genetski algoritam za planiranje proizvodnje

(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

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

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Particle swarm optimization approach for modelling a turning 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