Abstract
In this paper, an attempt has been made to develop an accurate mathematical model for predicting the surface roughness in end milling of P20 mould steel using artificial neural networks (ANN). For training and testing of the ANN model, a number of experiments have been carried out using Taguchi’s orthogonal array in the design of experiments (DOE). The cutting parameters used are nose radius, cutting speed, cutting feed, axial depth of cut and radial depth of cut. The ANN model was developed using multilayer perceptron (MLP) network for nonlinear mapping between the input and the output parameters. The adequacy of the developed model is verified using coefficient of determination (R). It was found that the R 2 value is 1 for surface roughness. To judge the ability and efficiency of the ANN model, percentage deviation and average percentage deviation has been used. The research showed acceptable prediction results for the ANN model.
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