Prediction of tool wear using regression and artificial neural network models in end milling of AISI 304 Austenitic Stainless Steel

Tool wear prediction plays an important role in industry for higher productivity and product quality. Flank wear of cutting tools is often selected as the tool life criterion as it determines the diametric accuracy of machining, its stability and reliability. This paper focuses on two different models, namely, regression mathematical and artificial neural network (ANN) models for predicting tool wear. In the present work, flank wear is taken as the response (output) variable measured during milling, while helix angle, spindle speed, feed rate and depth of cut are taken as input parameters. The Design of Experiments (DOE) techniques developed for four factors at five levels to conduct experiments. Experiments have been conducted for measuring tool wear based on the DOE technique in a vertical machining center on AISI 304 steel using solid carbide end mill cutter. The experimental values are used in Six Sigma software for finding the coefficients to develop the regression model. The experimentally measured values are also used to train the feed forward back propagation artificial neural network (ANN) for prediction of tool wear. Predicted values of response by both models, i.e. regression and ANN are compared with the experimental values. The predictive neural network model was found to be capable of better predictions of tool flank wear within the trained range.