Multi-objective parametric optimization for high surface quality and process efficiency in micro-grinding

In this study, for the selection of maximum material removal rate and minimum surface roughness ( R a ) in micro-grinding of aluminum alloy through multi-response optimization, two optimization approaches are proposed based on statistical analysis and genetic algorithm. The statistical analysis–based approach applies response surface methodology according to the analysis of variance to propose a mathematical model for R a . In addition, the individual desirability of material removal rate, R a , and the global desirability function are calculated, and the inverse analysis is conducted to locate input setting giving maximum desirability function. The genetic algorithm–based approach uses the improved multi-objective particle swarm optimization with the experimental data trained by support vector machine. To demonstrate that the material microstructure is a significant parameter for material removal rate and R a , the models with and without Taylor factor consideration are developed and compared. The optimized results achieved from both response surface methodology and improved multi-objective particle swarm optimization demonstrate that the consideration of Taylor factor can significantly improve the optimization process to achieve the maximum material removal rate and minimum R a