Surface Roughness Optimization in Aluminium Alloy Milling: A Taguchi Based Analysis of Dovetail Cutter Parameters and Tool Wear Prediction - Chan Kian Wui; Saliza Azlina Osman; Agustinus Purna Irawan; Tay Sin Kiat; Osman, Shahrul Azmir (International Journal of Integrated Engineering, 2026)
This study presents a comprehensive optimization of machining parameters for surface roughness minimization in aluminium alloy milling using dovetail cutters. The Taguchi L18 orthogonal array design was employed to systematically investigate the effects of four critical parameters: number of cutter flutes (2-3), spindle speed (7000-9000 rpm), feed rate (500-900 mm/min), and depth of cut (0.1-0.3 mm). Analysis of variance (ANOVA) was conducted to determine parameter significance and contribution to surface roughness variation. Experimental results revealed that cutter flute geometry constitutes the dominant factor, contributing 84.67% of total variance (p < 0.001) with a 44.0% improvement achieved by transitioning from 2-flute to 3-flute configurations. Feed rate emerged as the secondary influential parameter (5.62% contribution, p = 0.024), while spindle speed showed marginal significance (4.11% contribution, p = 0.052). Depth of cut demonstrated minimal influence (0.52% contribution, p = 0.613) within the tested range. The optimal parameter combination of 3-flute cutter, 8000 rpm spindle speed, 700 mm/min feed rate, and 0.3 mm depth of cut achieved minimum surface roughness of 0.174 μm with 89.7% prediction accuracy. Tool wear analysis using vision measuring machine technology over 6000 minutes revealed three distinct phases: break-in (0-600 minutes), stable operation (600-5400 minutes), and accelerated wear (5400-6000 minutes). Surface roughness deteriorated 105% during the final phase, establishing 5400 minutes as the optimal tool replacement interval for maintaining surface quality. The signal-to-noise ratio analysis confirmed process robustness, with the optimal configuration achieving 15.61 dB, indicating minimal sensitivity to environmental variations. This integrated approach combining Taguchi optimization with predictive tool wear management provides a robust framework for enhancing CNC milling performance in aluminium alloy applications. The methodology successfully identifies parameter hierarchy while establishing practical guidelines for industrial implementation, contributing to improved surface quality and operational efficiency in precision manufacturing.