ArticleScientific reports2026
Hybrid RSM-SF-AHP-fuzzy MARCOS approach for multi-response optimization of WAAM-fabricated Ni-SS bimetallics in face milling.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study develops Wire Arc Additively Manufactured (WAAM) Nickel-Stainless Steel bimetallic samples with silicon enhancement and investigates their face-milling machinability using a hybrid multi-criteria optimization framework. Seventeen machining trials were designed and conducted using a Response Surface Methodology (RSM)-based Central Composite Design (CCD) to evaluate the effects of cutting speed (8000-9500 rpm), feed rate (0.1-0.25 mm/tooth), depth of cut (0.5-1.5 mm), and tool flute count (two and four flutes) on surface roughness (Ra), material removal rate (MRR), power consumption (Pc), and cutting force (CF). A three-stage hybrid methodology was implemented, in which RSM modelled machinability behaviour and identified significant process parameters, Spherical Fuzzy AHP (SF-AHP) assigned criteria weights under uncertainty, and Fuzzy MARCOS ranked the machining alternatives. Run 1 (9500 rpm, 0.1 mm/tooth, 1.5 mm, four flutes) was identified as the optimal setting, exhibiting lower surface roughness (~ 0.6 μm) and higher MRR (~ 35.6 mm³/min), whereas Run 6 (8750 rpm, 0.175 mm/tooth, 1 mm, two flutes) showed comparatively poorer performance with higher Ra (~ 0.8 μm), reduced MRR (~ 34 mm³/min), and less favorable power consumption and cutting force, indicating weaker overall machinability. Cutting speed and depth of cut strongly influenced Ra and MRR, while feed rate and flute configuration primarily affected Pc and CF. Validation through confirmation experiments, criteria-weight verification, and ranking consistency demonstrated strong agreement between predicted and measured performance. Although limited to a specific silicon-enhanced Ni-SS bimetallic system and finite experimental range, the proposed framework provides a practical and robust strategy for multi-response machinability optimization of WAAM components, offering industrial benefits such as improved surface finish, reduced power consumption, enhanced productivity, and better machining stability.
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