Hybrid Optimization for Machinability Enhancement during Green Machining of Stainless Steel

Stainless steel 304 is one of the most promising materials for many industrial applications. Its machinability is poor whether machined in any environment. Conventional machining causes environmental degradation as well. In this paper, sustainable machining of SS304 using green lubricant is presented. Experiments have been conducted on Taguchi’s robust design of experiment technique. For machinability enhancement, a hybrid optimization technique VIKOR-Regression-PSO is employed. Machinability indicators that have been considered are tool wear, surface roughness, and chip reduction coefficient. Cutting speed, depth of cut, and feed rate have been considered as the variable machining parameters in this work. The hybrid optimization has been found very effective and provided a set of optimum machining parameters i.e. cutting speed-70m/min; feed rate-0.1mm/rev; depth of cut-0.5mm for the best values of machinability indicators i.e. tool wear-249.22 µm, roughness-11.08 µm, chip reduction coefficient-2.26.