Modeling and prediction of cutting forces during the turning of red brass (C23000) using ANN and regression analysis

The life of a cutting tool is greatly influenced by the forces acting on it during a cutting operation. A machining operation is a complex process. It is very difficult to develop a comprehensive model involving all the parameters. The present study aims to develop a model to investigate the effects of cutting parameters (speed, depth of cut and feed rate) on the cutting forces during the turning operation of red brass (C23000) using high speed steel (HSS) tool. The experimental results are based on full factorial design methodology to increase the reliability and confidence limit of the data. Artificial neural network and multiple regression approaches were used to model the cutting forces on the basis of cutting parameters. In order to check the adequacy of the regression model, analysis of variance (ANOVA) was used. It was clear from the ANOVA that the regression model is capable to predict the cutting forces with high accuracy. However, ANN model was found to be more accurate than the regression model.