Achieving sustainable machining of titanium grade 3 alloy through optimization using grey relational analysis (GRA)

Abstract

In the contemporary landscape of advanced manufacturing, there is an increasing demand for machining processes that excel in both quality and energy efficiency. One of the prime objectives is to excel in an era of machining practices that are environmentally conscious and economically sustainable. This research addresses this challenge by undertaking a comprehensive exploration of multi-objective optimization, specifically tailored to address the machining challenges presented by Titanium Grade 3 alloy. A robust Taguchi-Grey integrated approach was adopted with research aim to strike an optimum balance among specific cutting energy, tool wear, surface finish, and material removal rate. The impact of machining inputs i.e., feed rate, cutting speed, and depth of cut were analyzed. Best machining setting was identified using grey relational analysis. Feed rate was identified as the most influential member affecting grey relational grade having contribution ratio of 73.95%. Furthermore, surface plots and contour plots were developed for aid of machinists on job floor in selection of best cutting conditions. Harnessing the usefulness of response surface optimization, the machinability was further enhanced by 9% reduction in specific cutting energy and 7% improvement in tool wear. However, it is imperative to acknowledge the trade-offs as a result of MOO; surface roughness increases by 12%, potentially necessitating additional post-processing steps.