Development of a Multi-Objective Optimization Model for the Hard Turning of SKD11 Steel with Nanofluid-Al₂O₃ Minimum Quantity Lubrication Using RSM and PSO

This study focuses on the effects of the hard turning of SKD11 steel with nanofluid-based Minimum Quantity Lubrication (MQL) on machining efficiency, optimizing the surface roughness (Ra) and Material Removal Rate (MRR). For this purpose, a hybrid Response Surface Methodology (RSM) and Particle Swarm Optimization (PSO) approach are utilized for the SKD11 hard turning under Al₂O₃ nanofluid-MQL conditions. Initially, 27 experiments were conducted using a Box-Behnken design with Al₂O₃ concentration (0–3% wt), cutting speed (v) of 60–100 m/min, depth of cut (ap) ranging from 0.2 to 0.6 mm, and feed rate (f) from 0.1 to 0.2 mm/rev, followed by four additional runs, totaling 31 experiments. The resulting RSM models for Ra and MRR achieved high accuracy with an R² value of 97.69%. The PSO optimization identified extreme solutions: A minimum Ra of 0.43 µm at 3.0% Al₂O₃, v of 95 m/min, ap of 0.4 mm, f of 0.12 mm/rev, and a maximum MRR of 9000 mm³/min at 1.5% Al₂O₃, v of 100 m/min, ap of 0.6 mm, and f of 0.15 mm/rev. Additionally, a balanced multi-objective solution was obtained at 2.0% Al₂O₃: 98 m/min, 0.5 mm, and 0.14 mm/rev, yielding Ra ≈ 0.55 µm and MRR ≈ 8400 mm³/min. The proposed RSM-PSO hybrid approach effectively balances surface quality and productivity, outperforming traditional methods. The findings highlight the benefits of iterative refinement and provide practical parameter optimization for the sustainable machining of hardened steels.