An experimental study of multi-response optimization for drilling performance in WAAM-fabricated aluminum using RSM...

This study develops a hybrid multi-objective optimization framework to enhance the drilling performance of Wire Arc Additive Manufacturing (WAAM)-fabricated aluminum components, addressing the need for precision post-processing of additively manufactured parts. Response Surface Methodology (RSM) is employed to model the influence of drilling parameters-cutting speed, feed rate, tool point angle, and coolant composition-on surface roughness (Ra), material removal rate (MRR), hole diameter accuracy (HDA), and energy consumption (EC). Intuitionistic Fuzzy MARCOS (IF-MARCOS) is integrated to enable uncertainty-aware multi-criteria decision-making and global ranking of alternatives. The optimal conditions were identified as a cutting speed of 50 m/min, a feed rate of 0.3 mm/rev, a tool point angle of 135 °, and an oil-based coolant. To clarify the improvement basis, the worst-ranked condition, consisting of 150 m/min cutting speed, 0.1 mm/rev feed rate, 90° tool point angle, and water-based coolant, was used as the baseline/reference condition. Compared with this baseline, the optimal condition reduced Ra from 1.6667 μm to 1.2334 μm (26.0% reduction) and EC from 280 W to 120 W (57.14% reduction). In addition, MRR increased from 70 to 72 cm³/min, giving a 2.86% improvement, while HDA improved from 90% to 96%, corresponding to a 6.67% improvement. The results demonstrate that a moderate feed rate, stable tool geometry, and lubrication-dominant cooling improve machining stability, surface integrity, dimensional accuracy, and energy efficiency. The proposed RSM-IF-MARCOS framework provides a robust, uncertainty-aware optimization approach, offering both methodological advancement and practical guidance for the sustainable machining of WAAM-fabricated aluminum components.