Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel

Authors:
Ulaş Çaydaş;Sami Ekici
Affiliations:
Technical Education Faculty, Department of Manufacturing, University of Firat, Elazig, Turkey 23119;Technical Education Faculty, Department of Electric, University of Firat, Elazig, Turkey 23119
Venue:
Journal of Intelligent Manufacturing
Year:
2012

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Abstract

In the present investigation, three different type of support vector machines (SVMs) tools such as least square SVM (LS-SVM), Spider SVM and SVM-KM and an artificial neural network (ANN) model were developed to estimate the surface roughness values of AISI 304 austenitic stainless steel in CNC turning operation. In the development of predictive models, turning parameters of cutting speed, feed rate and depth of cut were considered as model variables. For this purpose, a three-level full factorial design of experiments (DOE) method was used to collect surface roughness values. A feedforward neural network based on backpropagation algorithm was a multilayered architecture made up of 15 hidden neurons placed between input and output layers. The prediction results showed that the all used SVMs results were better than ANN with high correlations between the prediction and experimentally measured values.