Multi Objective optimization in micromilling of titanium alloy (Ti6Al4V) under Dry, Wet and MQL using gray relational analysis

Abstract

Micromilling is among the most significant and extensively employed types of micromechanical machining, attributed to its ability to reach higher precision requirements, even when machining high-strength materials. By its biocompatibility, titanium alloy Ti6Al4V has now been frequently utilized in industries such as the medical field as prosthesis & surgical instruments. Titanium alloys are notorious for having poor thermal conductivity and toughness at elevated temperature. Consequently their intrinsic mechanical and thermal qualities (which induce severe tool wear and limit tool life), lower surface quality, and reduced productivity. Because of cooling and lubricating effects, the use of various cutting conditions helps to the elevation of desired reactions, particularly in the case of difficult to cut materials like Ti6Al4V. I investigate the best machining parameters for micromilling Ti-6Al-4V alloy under multiple cutting conditions in this research. Micromilling experiments were conducted with uncoated tungsten carbide tool in the dry, mql, and wet settings to optimize five response parameters: cutting forces (C.F), tool wear rate (R), Surface roughness Ra, up burr (UB), and down burr (DB). A multi-objective function was designed using grey relational analysis (GRA). The created multi-objective function was optimized via response surface optimization, and the optimal cutting condition was determined. According to the ANOVA, cutting conditions were revealed to be the most significant aspect influencing the multi-objective function's grey relational grade (GRG).