Optimization of process parameters for turning of titanium alloy (Grade II) in MQL environment using multi-CI algorithm

Abstract The advancement of materials science during the last few decades has led to development many hard-to-machine materials, such as titanium, stainless steel, high-strength temperature-resistant alloys, ceramics, refractories, fibre-reinforced composites, and superalloys. Titanium is a prominent material widely used for several industrial applications. However, it poor machinability hence efficient machining critical. Machining titanium alloy (Grade II) in minimum quantity lubrication (MQL) environment one recent approaches towards sustainable manufacturing. This problem been solved using various experimental investigation, desirability, with optimization algorithms. In group socio-inspired algorithm, an artificial intelligence (AI)-based methodology referred Cohort Intelligence (CI) developed. this paper, CI algorithm Multi-CI have applied optimizing process parameters associated turning MQL environment. performance these algorithms exceedingly better compared particle swarm desirability approaches. analysis regarding convergence run time all also discussed. It important mention that environment, achieved 8% minimization cutting force, 42% tool wear, 38% tool-chip contact length, 15% surface roughness when PSO. For approaches, 12% 47% 53% 40% 20% were attained, respectively.