<![CDATA[This study investigates a hybrid cooling–lubrication approach combining nanofluid-assisted minimum quantity lubrication (MQL) with cold-air cooling in the hard milling of SKD11 steel, aiming to improve both surface quality and productivity. Response surface methodology (RSM) was employed to model the relationships between machining parameters and two key performance indicators, namely surface roughness (Ra) and material removal rate (MRR). The developed models were integrated with particle swarm optimization (PSO) for multi-objective optimization. Compared with conventional MQL, the hybrid approach reduced Ra by approximately 5–10% under similar cutting conditions, indicating enhanced cooling and lubrication performance. The optimization results revealed distinct trade-offs between surface quality and productivity. When surface quality was prioritized (wRa = 0.7), a minimum Ra of 0.160 µm was achieved with a relatively low MRR of about 653 mm³/min. In contrast, both the balanced (wRa = 0.5) and productivity-oriented (wRa = 0.3) scenarios converged to the same optimal solution (Ra ≈ 0.245 µm, MRR ≈ 1790 mm³/min). This convergence suggests that MRR dominates the optimization when its weighting is comparable to or higher than that of Ra, indicating a plateau region on the trade-off surface. The proposed RSM–PSO framework provides both an effective optimization approach and new insight into balancing surface integrity and productivity in hybrid-cooled hard milling]]>
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