Multi-response optimization for sequential application of erosion–abrasion in face grinding configuration

Abstract

The sequential application of erosion–abrasion is an innovative technique for shaping of electrically conductive objects but the integrated effect of erosion–abrasion in shaping makes the process complex resulting selection of input parameters are difficult for manufactures as well as researchers. The aim of present study is to optimize the parameters such as current, on-time, off-time, RPM, and grit number to minimize the material removal and surface roughness for erosion–abrasion process. For this, neural network technique is applied to develop a model whereas Taguchi methodology (TM) and genetic algorithm (GA) are tested for optimization of parameters. The results show that GA gives multiple optimal data as compared to single optimal data of TM. The results also show that optimal data significantly matched with experimental data with absolute percentage errors within acceptable ranges.