Balancing Cutting Temperature, Force, and Surface Integrity in Titanium Alloy Machining: A Multi Objective Framework

Abstract

Titanium alloys, particularly Ti-6Al-4V Extra Low Interstitial (ELI), are widely used in aerospace, biomedical, and marine applications due to their high strength-to-weight ratio, fracture toughness, and corrosion resistance. However, poor thermal conductivity and rapid tool wear make them difficult to machine. In this study, the effects of cutting speed, feed, depth of cut, and tool nose radius on cutting temperature, cutting force, and surface roughness were investigated during dry turning of Ti-6Al-4V (ELI). A full factorial design comprising 81 experiments was conducted. The measured cutting temperature ranged from 46°C to 228°C, cutting force from 12 kgf to 71 kgf, and surface roughness from 0.307 µm to 1.832 µm. Response Surface Methodology (RSM) and ANOVA were applied to quantify parameter contributions, revealing feed rate and cutting speed as dominant factors for cutting temperature, while tool nose radius significantly influenced surface roughness. Regression models predicted responses with an average error of 7.63%, whereas Artificial Neural Network (ANN) models achieved improved accuracy with an average error of 3.75%. Pareto front analysis demonstrated optimal trade-offs, such as achieving Ra = 0.33 µm at T = 105.8°C with a 1.2 mm nose radius and moderate feed. The developed models were further interfaced with Particle Swarm Optimization (PSO) to minimize cutting temperature and force simultaneously. The results confirm that integrating statistical modeling, ANN prediction, and PSO optimization provides a robust framework for enhancing machinability and sustainability in titanium alloy turning.