Modelling and simulation of spark assisted diamond face grinding of tungsten carbide-cobalt composite using ANN

Abstract

The aim of this study is to develop an artificial neural network (ANN) model for spark assisted diamond face grinding (SADFG) of cobalt bonded tungsten carbide (WC-Co) composite to predict the material removal rate (MRR) and average surface roughness (Ra). The experiments were conducted on a self-developed face grinding setup, which is attached with EDM machine. A bronze metal bonded diamond wheel is used for experimentations. All the experiments were performed according to the central rotatable design. The current, pulse on-time, duty factor and wheel speed were taken as input process parameters and responses are measured in terms of MRR and Ra. Central rotatable design is used for experimentation. The obtained experimental data set was used to train the ANN model. The ANN architecture with back propagation algorithm has been used for modelling of process parameters of SADFG process. It has been found that the developed ANN model is capable to predict the MRR and Ra with absolute average percentage error of 10.40% and 6.81%, respectively. It has been also found that wheel speed at 1,300 RPM is suitable for achieving of the better surface finish while duty factor at 0.70 has been found more appropriate for higher MRR.