Parametric optimisation of milling process for the machining of carbon nanotubes-based hybrid aluminium composite

Abstract

Development of new materials is a never-ending process in material science. Novel materials need to have good machinability for it to be industrially applicable. In the current work, multi-walled carbon nanotubes (MW-CNTs) and silicon carbide (SiC) reinforced hybrid aluminium composite (HAC) is developed using stir casting route. Scanning electron microscopy (SEM) images reveal proper mixing with very little agglomeration of reinforcement particles. Hardness and corrosion resistance were also improved in comparison with the base alloy. The developed composite was machined using conventional milling machine following L27 Taguchi orthogonal array experimental design. Material removal rate (MRR) and surface roughness (SR) were optimised using Taguchi signal-to-noise (S/N) ratio, grey relation analysis (GRA) and dragonfly algorithm (DA). Single objective optimisation revealed that low spindle speed and high feed rate and depth of cut would result in maximum MRR while high spindle speed, feed rate and depth of cut would result in minimum SR. DA could successfully predict the optimum MRR with less than [Formula: see text] error suggesting it to be a reliable tool for optimisation problems. Pareto front for multi-objective optimisation revealed that a proportionate compromise between MRR and SR can be made to identify optimum processing parameters in the experimental space.