Optimization of machining parameters in CNC end milling for surface roughness and material removal rate on AISI 4130 low-alloy steel using a Taguchi-based Gray Relational Analysis

Abstract

This study applies an integrated Taguchi-Gray Relational Analysis (GRA) method to optimize CNC end milling parameters for AISI 4130 low-alloy steel, aiming to simultaneously maximize Material Removal Rate (MRR) and minimize surface roughness (Ra). Experiments were designed using an L9 orthogonal array, varying spindle speed (1200, 1400, 1600 rpm), feed rate (0.009, 0.014, 0.019 mm/tooth), and depth of cut (0.1, 0.15, 0.2 mm) under dry conditions with a high-speed steel end mill. The multi-response data were converted into a single Gray Relational Grade (GRG) for optimization. Analysis identified the optimal parameter set as 1600 rpm spindle speed, 0.019 mm/tooth feed rate, and 0.2 mm depth of cut. Analysis of Variance (ANOVA) on the GRG indicated spindle speed as the most statistically significant factor, accounting for 83.73% of the performance variation, followed by feed rate (10.11%) and depth of cut (2.25%). Confirmation experiments at the optimal settings validated the results, achieving a GRG improvement of 10.7% over the initial best run, with MRR of 31.65 cm3/min and Ra of 0.37 µm. The findings provide a practical framework for enhancing productivity and surface finish in the machining of AISI 4130 steel components.