Modelling and multiobjective optimization for productivity improvement in high speed milling of Ti–6Al–4V using RSM and GA

Abstract

Productivity can be improved in machining by achieving higher material removal rate (MRR) and better surface finish at lower power consumption along with higher tool life. Present work focuses on analyzing power consumption, material removal rate; surface roughness and tool wear in high speed milling of Ti–6Al–4V using response surface methodology. Models are developed with experimental data measured after performing face milling operation sequentially using design of experiments. Developed models are validated and reformed using Analysis of variance (ANOVA) and stepwise backward elimination method. Developed models showed correlation coefficient (R2) more than 95% which means models can best explain the experimental data. Further, multiobjective optimization is performed to minimize power consumption; surface roughness and tool wear as well as to maximize MRR using response optimizer with desirability approach. Optimum process parameters obtained are: cutting speed = 133.5 m/min, feed rate = 0.14 mm/tooth and depth of cut = 2.33 mm. Validation of optimized results is done with three confirmation experiments at the optimum conditions and the responses are taken as average of the three confirmation experiments. Additionally, Pareto optimal points are found for conflicting objective functions using multiobjective genetic algorithm.