Predictive analysis and performance measures optimisation in sustainable abrasive jet machining

Abstract Within the United Nations Sustainable Development Goals (SDGs), Goal 9 calls for industries to be made sustainable through increased resource-use efficiency and adoption of environmentally sound processes, while Goal 12 recognises that sustainable manufacturing promotes responsible production and reduces waste. Building on this motivation, the present study demonstrates the feasibility of using unprocessed beach sand as an abrasive for precision through-hole drilling of mild steel in Abrasive Jet Machining (AJM), supported by predictive modelling and multi-objective optimisation. A custom AJM system was developed, and experiments were conducted using a Taguchi L27 orthogonal array to investigate the effects of control parameters on responses: Material Removal Rate (MRR) and Kerf Taper Angle (KTA). Experimental runs revealed stable MRR and reduced KTA, outcomes which were not previously reported for ductile steel using natural abrasives. Random Forest and Extreme Gradient Boosting (XGBoost) models achieved high predictive accuracy (R 2 > 0.95 for MRR, >0.80 for KTA), while independent multi-criteria decision-making (MCDM) methods converged on the same optimal parameter set. Scanning electron microscopy (SEM) images at these settings showed sharper edges and improved surface integrity. By replacing conventional abrasives such as silicon carbide and aluminium oxide with locally available beach sand, the work addresses resource efficiency and waste reduction. The integration of sustainable abrasive selection, robust predictive modelling, and decision-driven optimisation may thus present a viable pathway towards greener, high-precision machining of ductile metals.