Investigation of Milling Parameters Effect on Material Removal Rate Using Taguchi and Artificial Neural Network Techniques

Abstract

The Artificial Neural Network (ANN) and numerical methods are used widely for modeling and predict the performance of manufacturing technologies. In this paper, the influence of milling parameters (spindle speed (rpm), feed rate (mm/min) and tool diameter (mm)) on material removal rate were studied based on Taguchi design of experiments method using (L16) orthogonal array with 3 factor and 4 levels and Neural Network technique with two hidden layers and neurons. The experimental data were tested with analysis of variance and artificial neural network model has been proposed to predict the responses. Analysis of variance result shows that tool diameters were the most significant factors that effect on material removal rate. The predicted results show a good agreement between experimental and predicted values with mean squared error equal to (0.000001), (0.00003025), (0.002601) and (0.006889) respectively, which produce flexibility to the manufacturing industries to select the best setting based on applications.