Prediction of Cutting Forces Obtained through Cryo-Treated and Untreated Cutting Tools Using Optimum ANN Determined by Taguchi Design

Abstract

This experimental and statistical study addresses the prediction of cutting forces by using the optimum Artificial Neural Network employed by Taguchi design. For this purpose, input and output transfer function and training algorithm were selected as control parameters, while Mean Square Error was chosen as output parameters for evaluating optimum ANN structure with S/N ratios. ANN structure was optimized through Taguchi L9 orthogonal design, which occurred 5 set-up for utilizing all training function. According to MSE values of S/N ratios, each set-up was compared with the obtained prediction of making values of cutting forces to the optimal result. For each set, the hidden transfer function, output transfer function and training function used in the optimal ANN structure were determined. The optimal ANN structure for cutting forces obtained in turning experiments were logsig transfer function in hidden layer, Tlm training function and pureline transfer function in output layer, while R square was at 0.999945. It was found that ANN based Taguchi orthogonal design was successful in evaluating the experimental results.