Optimizing the machining parameters for turning operation using cryo-treated M2 tool

Abstract In order to enhance efficiency, accuracy, and quality in CNC Turing operations, optimizing the machining parameters is crucial. This research implements five nature-inspired optimization techniques, which include Ant Colony Optimization (ACO), Bacterial Foraging Optimization (BFO), Firefly Algorithm (FFA), Harris Hawks Optimization (HHO), and Grey Wolf Optimizer (GWO), using Cryotread M2 tool steel. The aim of this research is to optimize the cutting force (Fc), surface roughness (Ra), and tool wear (VBmax) during the Turing of cryo-treated M2 tool steel. The machining parameters that were used were the cutting speed (Vc in RPM), feed rate (f in mm/rev), and depth of cut (D in mm). A technique has been devised which combines parameter selection, statistical and distribution analysis to determine the most effective optimization technique. Out of the five algorithms tested, the Grey Wolf Optimizer (GWO) has nullified the cutting force and surface roughness, and maximized tool wear enabling the highest machining efficiency. The results illustrate how GWO settles within an optimal region for exploration and exploitation, exemplifying its dependability when machining parameters are increased in CNC Turning. This research shows how high-performance machining processes are achieving longer tool life.