Investigating a novel Ag/ZnO based hybrid nanofluid for sustainable machining of inconel 718 under nanofluid based minimum quantity lubrication

Abstract Sustainable machining, with implementation of eco-friendly dry and minimum quantity lubrication (MQL) methods, has gained attraction of researchers and engineers to solve the environmental and health allied issues caused by bulk usage of the conventional cutting fluids. Nanofluids have recently became more obvious choice as a cutting fluid in MQL applications owing to their superior thermo-physical properties. In this work, a novel hybrid nanofluid, consisting of Ag coated ZnO (Ag/ZnO) nanoparticles in ethylene glycol base, has been proposed as cutting fluid for MQL application. Initially, the Ag/ZnO hybrid nanoparticles were synthesized by a chemical precursor method. Then, synthesized nanoparticles were blended in ethylene glycol at several volume concentrations (Φ = 0.05 % to 0.2 %). The nanofluids were characterized for their thermo-physical properties and stability criterion, and then a nanofluid with the best performance was selected for nanofluid based MQL (NFMQL) experiments. The performance of NFMQL was compared with dry, MQL environments during milling of difficult-to-cut Inconel 718 superalloy with PVD coated carbide inserts. Taguchi L9 orthogonal array was incorporated for experimental design to investigate the effect of cutting speed, feed rate and machining environment on the machining performance in terms of the average surface roughness and cutting temperature. Analysis of variance shown that the cutting environment contributed to the average surface roughness and cutting temperature by 24.52 % and 44.74 %, respectively. As compared to dry and MQL condition, the NFMQL improved the surface finish by 23.5 % and 13.07 %, respectively, and reduced the cutting temperature by 15.38 % and 8.56 %, respectively, owing to proposed hybrid nanofluids enhanced lubrication and heat dissipation properties. Furthermore, the field emission scanning electron microscopic (FESEM) images of used cutting tool inserts reveal that the NFMQL condition induces the minimum tool wear as compared with MQL and dry environments. Finally, the multi-response optimization was achieved through the implementation of Taguchi grey relational analysis (TGRA) with the optimum combination of cutting speed of 30 mm/min, feed rate of 0.036 mm/tooth, and NFMQL cutting environment.