Editorial analysis: For ML practitioners applying supervised models to manufacturing processes, this paper demonstrates a compact workflow that pairs regression models with multi-criteria optimization to predict and tune abrasive water jet machining outcomes. According to a Scientific Reports preprint published 29 June 2026, the study evaluates AWJM performance of an Al6061-0.5 wt.% B4C-1 wt.% ZrO2 hybrid composite fabricated using ultrasonic-assisted stir casting. The article reports experimental ranges of material removal rate (MRR) 7.86 to 15.24 mm^3/min, surface roughness (Ra) 3.220 to 3.980 um, and kerf taper angle (KTA) 0.142 degrees to 0.309 degrees. ANOVA results reported that abrasive flow rate (AFR) dominated MRR while abrasive jet cutting speed (AJCS) governed Ra and KTA. The paper applies a hybrid Grey Relational Analysis-Analytic Hierarchy Process (GRA-AHP) to optimize multiple objectives and develops SVR, RF, and MLP models to predict MRR, Ra, and KTA; the reported optimal AWJM setting was AFR 430 g/min, WJP 280 MPa, AJCS 80 mm/min, SOD 1.5 mm, and grit size 120 mesh.
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