Title: Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network

Authors: Farid Boukezzi; Rachid Noureddine; Ali Benamar; Farid Noureddine

Addresses: Université Mouley Tahar, BP 138 cité Ennasr, 20000 Saida, Algérie ' Institut de Maintenance et de Sécurité Industrielle, Université d'Oran 2 Mohamed Ben Ahmed, BP N°170 El M'Naouer, 31000 Oran, Algérie ' ENPO d'Oran, BP 1523 El-M'Naouer, 31000 Oran, Algérie ' ENI Tarbes, 47, avenue d'Azereix, 65016 Tarbes Cedex, France

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.

Keywords: prediction; surface roughness; artificial neural network; turning; cutting parameters; artificial neural network; ANN; modelling; simulation.

DOI: 10.1504/IJISE.2017.085227

International Journal of Industrial and Systems Engineering, 2017 Vol.26 No.4, pp.567 - 583

Received: 29 Mar 2016
Accepted: 29 Sep 2016
Published online: 17 Jul 2017 *

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