Exploration of nature inspired Grey wolf algorithm and Grey theory in machining of multiwall carbon nanotube/polymer nanocomposites

Abstract

The nanosize of reinforcement creates a better synergistic effect in epoxy composites. This effect enhances the desired properties of multifunctional components used in aircraft, automotive, sports, biomedical, sensors, etc. In this series, MWCNT/epoxy nanocomposites possess high strength, reduced weight, moisture and chemical resistant properties. The machining performance of MWCNT nanocomposites is remarkably distinct from metallic materials. This article explores the machining aspects of MWCNT composites using Grey relation analysis and relatively advanced nature-inspired Grey wolf optimization (GWO). Machining (milling) experiments consider a real-world problem framed on Taguchi L27 OA. The second-order polynomial regression equation shows a desirable agreement between experimental and predicted values. The average surface roughness, cutting force and MRR significantly achieved the desired enhancements. The optimal setting of GRA based Grey wolf optimizer are found as 0.5 wt.% MWCNT, 1500 rpm spindle speed, 50 mm/min feed rate and 3 mm depth of cut. It was noticed that the depth of cut plays a prominent role in affecting the machining characteristics. The outcomes of the confirmatory test show that GRA-GWO is more feasible than the traditional GRA method.