Multi-Objective Optimization of Machining Parameters in CNC Face Milling of Ti-6Al-4V Titanium Alloy Using Taguchi-Grey Relational Analysis

Abstract

Titanium alloy Ti-6Al-4V is widely utilized in aerospace, biomedical, and marine engineering due to its exceptional strength-to-weight ratio and corrosion resistance. However, its poor machinability, characterized by high cutting temperatures, rapid tool wear, and work-hardening tendencies, poses significant manufacturing challenges. This study presents a systematic multi-objective optimization methodology for CNC face milling of Ti-6Al-4V using uncoated carbide inserts under flood cooling conditions. Four critical machining parameters—cutting speed, feed rate, axial depth of cut, and radial depth of cut—were investigated at three distinct levels using an L27 orthogonal array based on Taguchi design of experiments. Grey Relational Analysis (GRA) coupled with Analysis of Variance (ANOVA) was implemented to optimize conflicting performance characteristics, specifically minimizing surface roughness (Ra) and maximizing material removal rate (MRR). The optimal parameter combination yielded a 32.4% improvement in overall Grey Relational Grade, with feed rate identified as the most statistically significant factor influencing surface quality.

Cite as:

V. Vanduskar& Avdhut Gujar. (2026). Multi-Objective Optimization of Machining Parameters in CNC Face Milling of Ti-6Al-4V Titanium Alloy Using Taguchi-Grey Relational Analysis. Research and Reviews on Experimental and Applied Mechanics, 9(2), 21–27. https://doi.org/10.5281/zenodo.21903749