Experimental investigations on surface integrity and chip morphology in hard tuning of AISI D3 steel under sustainable nanofluid-based minimum quantity lubrication

Efficient cooling and lubrication techniques are required to obtain sustainable machining of difficult-to-cut materials, which are the pillars of aerospace, automotive, medical and nuclear industries. Excessive cutting fluid is required and consumed in machining difficult-to-machine materials with high-pressure coolant supplies. Nanofluid-assisted minimum quantity lubrication (NFMQL) is adorned with improved machining performance and environmental sustainability. The present work addresses the surface integrity and chip morphology in finish hard turning of AISI D3 steel under NFMQL condition. The surface integrity aspects include microhardness, residual stress, white layer formation, machined surface morphology, and surface roughness. This experimental investigation aims to explore the feasibility of low-cost multilayer (TiCN/Al2O3/TiN) coated carbide tool in hard machining applications and to assess the propitious role of minimum quantity lubrication using graphene nanoparticles with enriched radiator coolant-based nano-cutting fluid for machinability improvement in hardened steel. Combined approach of central composite design—analysis of variance, desirability function analysis, and response surface methodology, has been subsequently employed for experimental investigation, predictive modelling, and optimization of surface roughness. With a motivational philosophy of “Go Green-Think Green-Act Green”, the work also deals with economic analysis and sustainability assessment under environmental-friendly NFMQL condition. It is expected that the found optimized parameters can contribute to machining end-outcomes such as an improved surface finish and reduced machining cost. The results showed that machining with NFMQL provided an effective cooling–lubrication strategy, safer and cleaner production, environmental friendliness and assisted in improving sustainability. In conclusion, the proposed NFMQL turning strategy is a robust method validated by statistical analysis for large industrial applications, especially in mould and die making sectors.