Multiresponse Optimization of Grinding AISI 316 Stainless Steel Using Grey Relational Analysis

The present study investigated the optimization of grinding AISI 316 stainless steel by the Taguchi and grey relational analysis under the three environments of dry, conventional, and cryogenic cooling. The performance characteristics considered are, the material removal rate (MRR), surface roughness (Ra), and grinding force (Ft). Experiments were conducted with Al2O3 (Aluminum Oxide) and Sol-Gel (SG) grinding wheels under different cutting conditions, such as work speed, depth of cut, and cooling environments. An orthogonal array L18 is used for the experimental design. The optimum levels of the machining parameters were predicted from the grey relational grade derived from the grey relational analysis. The optimization results indicate that grinding with the SG wheel under cryogenic cooling gives a better performance. The cooling environment is the most significant factor for effective grinding performance. The surface roughness reduced by 4.9%, MRR improved by 20%, and the grinding forces reduced by 2.2% realized from the confirmation experiment. The optimal results show that the Taguchi-Grey relational analysis was successful in improving the grinding performance.