Material-specific machining optimization of Ti6Al4V alloy under MQL: A sustainability-centric approach

Abstract

The manufacturing industry is undergoing rapid transformation driven by modern technologies, with a focus on producing high-quality products efficiently. In line with increasing sustainability concerns, researchers are actively exploring various green machining methods. Machining of aerospace alloys and subsequent process optimization remains challenging for their poor thermal conductivity and high chemical affinity at elevated temperatures. This study investigates the machinability of Ti6Al4V alloy under an MQL environment for sustainability. A total of 27 experiments were conducted, with the SVR model predicting surface roughness (Ra) with a mean absolute percentage error of 4.68 %. Parametric analysis revealed feed has the highest significant influence on Ra, followed by cutting speed and depth of cut. Finally, Jaya algorithm was used to optimize surface roughness, resulting in an optimal solution with a Ra value of 0.4812 µm at 120 m/min, feed of 0.05 mm/rev, and depth of cut of 0.2 mm.