A hybrid approach of NSGA-II and TOPSIS for minimising vibration and surface roughness in machining process

Abstract

Increasing vibration amplitude during end milling process can seriously affect the life of end mills and reduces surface finish. Spindle and worktable vibration has a significant influence on surface quality of machined components. This paper confronts and investigates the effect of machining and geometrical parameters (spindle speed, feed rate, axial depth of cut, radial depth of cut and radial rake angle) on spindle and worktable vibration in terms of acceleration amplitude and surface roughness. Experiments were conducted on aluminium alloy 6061-T6 with high-speed steel (HSS) end mill cutter based on the central composite design (CCD). Response surface methodology (RSM) was used to develop the predictive models and the adequacy of the models were verified using analysis of variance (ANOVA). Non-dominated sorting of genetic algorithm (NSGA-II) was adopted to solve the multi objective optimisation problem and the optimised results were resulted with a set of Pareto-optimal solutions. The multi criteria decision making method (MCDM) such as technique for order preference by similarity to ideal solution (TOPSIS) and analytical hierarchy process (AHP) were designed to rank the Pareto optimal solutions based on response of closeness coefficient values.