Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network
by Farid Boukezzi; Rachid Noureddine; Ali Benamar; Farid Noureddine
International Journal of Industrial and Systems Engineering (IJISE), Vol. 26, No. 4, 2017

Abstract: Surface roughness is a very important measurement in machining process and a determining factor describing the quality of machined surface. This research aims to analyse the effect of cutting parameters [cutting speed (v), feed rate (f) and depth of cut (d)] on the surface roughness in turning process. For that purpose, an artificial neural network (ANN) model was built to predict and simulate the surface roughness. The ANN model shows a good correlation between the predicted and the experimental surface roughness values, which indicates its validity and accuracy. A set of 27 experimental data on steel C38 using carbide P20 tool have been conducted in this study.

Online publication date: Mon, 17-Jul-2017

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