Premature endmill failure in aluminum 6061 milling increases production costs, machine downtime, and surface quality variation. Previous studies have generally been conducted under laboratory conditions; therefore, they do not fully represent parameter variations, machine conditions, operator involvement, and monitoring limitations in industrial shop-floor environments. This study aims to identify the dominant factors affecting endmill breakage and to determine the optimum cutting parameters. The research method integrates DMAIC Lean Six Sigma, Design of Experiments (DoE), FMEA, ANOVA, and cutting force analysis. The results show that feed per tooth and depth of cut are the dominant factors affecting endmill breakage frequency. The optimum combination was obtained at the low cutting-speed level, feed per tooth of 0.10 mm/tooth, and depth of cut of 2 mm, with a predicted Breaks_out_of_10 value of 1.07. Based on the optimization model and cutting force analysis, this combination provides a predictive estimate of a 30–40% reduction in breakage compared with the baseline condition. The contribution of this study lies in integrating process improvement, statistical optimization, and mechanistic interpretation to support tool life control in industrial milling processes.
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