Predictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression

This paper focuses on developing empirical models for predicting surface roughness, tool wear and power required in turning operations. These response parameters are mainly dependent upon cutting velocity, feed and cutting time. Three competing data mining techniques, response surface methodology (RSM), artificial neural networks (ANN) and support vector regression (SVR), are applied in developing the empirical models. The data of 27 experiments have been used to generate, compare and evaluate the proposed models of tool wear, power required and surface roughness for the selected tool/material combination. Testing results demonstrate that the models developed in this research are suitable for predicting the response parameters with a satisfactory goodness of fit. It has been found that ANN and SVR models are much better than regression and RSM models for predicting the three response parameters. Finally, some future research directions are outlined.