Prediction of Surface roughness & Material Removal Rate for machining of P20 Steel in CNC milling using Artificial Neural Networks

Abstract

In this paper an attempt is made to predict Material Removal Rate and Surface roughness in CNC milling of P20 steel using Artificial Neural Networks (ANN). Taguchi’s L50 orthogonal array is used to design the experiments. The cutting parameters cutting speed, feed, axial depth of cut, radial depth of cut and nose radius is taken as input parameters and Material removal rate and Surface roughness are taken as output parameters. The ANN model is modelled using Multilayer Perceptron Network for nonlinear mapping between the input and output parameters. The developed model is verified using Regression coefficient(R) and it is found that R2 value is 1. From the results it is seen that ANN predicted values are close to the experimental values indicates that the developed model can be effectively used to predict the Material Removal Rate and Surface Roughness of P20 steel.