Thermal and tribological performance of CuO and olive oil nanofluids in sustainable hard turning

Hardened steels like SKD 11 are essential in high-stress industrial applications but pose machining challenges due to excessive heat generation, friction-induced tool wear, and the environmental impact of conventional mineral oil-based cutting fluids. This study develops and evaluates eco-friendly CuO/olive oil-based nanofluids for sustainable machining under Minimum Quantity Lubrication (MQL), with novelty in linking improved machinability to reduced cutting fluid consumption through an integrated performance–sustainability assessment. CuO nanoparticles were synthesized via the co-precipitation method and dispersed in olive oil at varying concentrations up to 1 wt.%. Compared to pure olive oil, the nanofluid with 1 wt.% CuO exhibited a 15.29% increase in thermal conductivity, a 25.22% increase in thermal diffusivity, a 24.47% increase in kinematic viscosity, and a 36.6% reduction in contact angle—collectively enhancing heat transfer, lubrication, and wettability. Machining experiments under MQL with these nanofluids showed reduced cutting temperature, friction, better chip morphology, and lower tool wear. The optimal cutting parameters—cutting speed of 134 m/min, feed rate of 0.137 mm/rev, and MQL with 1 wt.% nanofluid—were determined using Grey Relational Analysis (GRA), achieving a prediction accuracy with only 1.097% error. Correlation analysis revealed that higher kinematic viscosity (− 0.999), thermal conductivity (− 0.986), and lower contact angle (− 0.999) contribute significantly to reducing cutting temperature and enhancing cutting performance. SEM and EDX analyses confirm that CuO/olive oil-based nanofluids reduce friction and material transfer via tribo-film formation and microrolling effects, lowering flank wear from 300 μm (dry) to 170 μm (oil), and further to 105–40 μm at 0.5–1.0 wt.% CuO. MQL using these nanofluids reduces fluid consumption by up to 80%, enhancing machinability and sustainable manufacturing.