A comparative study of the ANN and RSM models for predicting process parameters during WEDC of SiCp/6061 Al MMC

Abstract

Metal matrix composites (MMCs) have found many successful industrial applications in recent past as high-technology materials due to their properties. Wire electric discharge cutting (WEDC) process is considered to be one of the most suitable processes for machining MMCs. Lot of research work has been done on WEDC, but very few investigations have been done on WEDC of MMCs. This paper reports work on the analysis of material removal rate (MRR) and cutting width (kerf) during WEDC of 6061 Al MMC reinforced with silicon carbide particles (i.e. SiCp/6061 Al). Four WEDC parameters namely servo voltage (SV), pulse-on time (TON), pulse-off time (TOFF) and wire feed rate (WF) were chosen as machining process parameters. Artificial neural network (ANN) models and response surface methodology (RSM) models were developed to predict the MRR and kerf using Box-Behnken design (BBD) to generate the input/output database. It was observed that prediction of responses from both models closely agree with the experimental values. The ANN models and RSM models for WEDC of MMC were compared with each other on the basis of prediction accuracy which shows that ANN models are more accurate than RSM models for MRR and kerf because the values of percentage absolute errors are higher for RSM models than ANN models. © Springer-Verlag London 2013.