Comparisons of neural network models on surface roughness in electrical discharge machining

Abstract

In this work, two different artificial neural network (ANN) models — back-propagation neural network (BPN) and radial basis function neural network (RBFN) — are presented for the prediction of surface roughness in die sinking electrical discharge machining (EDM). The pulse current (Ip), the pulse duration (Ton), and duty cycle (τ) are chosen as input variables with a constant voltage of 50 volt, and surface roughness is the output parameters of the model. A widespread series of EDM experiments was conducted on AISI D2 steel to acquire the data for training and testing and it was found that the neural models could predict the process performance with reasonable accuracy, under varying machining conditions. However, RBFN is faster than the BPNs and the BPN is reasonably more accurate. Moreover, they can be considered as valuable tools for EDM, by giving reliable predictions and provide a possible way to avoid time-and money-consuming experiments.