Multi-Objective Optimization of Micro-Milling Titanium Alloy Ti-3Al-2.5V (Grade 9) Using Taguchi-Grey Relation Integrated Approach

Abstract

This study aims to optimize the cutting parameters for the micro-milling of titanium grade 9 (Ti-3Al-2.5V). The research employs Grey Relational Analysis (GRA) and Response Surface Methodology (RSM) techniques to find the optimal combination of cutting parameters to simultaneously minimize surface roughness, burr width, burr length, and tool wear, which are selected process outcomes. The findings from Grey Relational Analysis (GRA) identify experiment number 6, with cutting conditions of f (µm/tooth) = 0.45, Vc (m/min) = 25, and ap (µm) = 60, as the most productive experiment. Analysis of variance (ANOVA) is conducted to assess the significance and influence of the process cutting parameters on different process outcomes. ANOVA reveals that the feed rate and cutting speed are the most influential input parameters, with a contribution ratio (CR) of 24.08% and 14.62%, respectively. Furthermore, ANOVA indicates that the interaction among the process parameters also significantly influences the process outcomes alongside the individual cutting parameters. The optimized combination of cutting parameters obtained through the RSM technique produces superior results in terms of reducing the process outcomes. Compared to the best run identified by Grey Relational Analysis, there is a remarkable 36.25% reduction in burr width and an 18.41% reduction in burr length, almost half of the reduction achieved in burr width. Additionally, there is a 16.11% and 14.60% reduction in surface roughness and tool wear, respectively.