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The current research addresses the morphology of the surface and chip when functionally graded (FG) specimen is hard to turn under nanofluid-assisted minimum quantity lubrication (NFMQL) conditions. Machining trials were performed by the help of Taguchi L 27  design using FG specimens varying machining conditions, i.e. spindle speed (45, 110 and 190 m/min), axial feed rate (0.15, 0.25 and 0.35 mm/rev) and depth of cut (0.1, 0.2 and 0.3 mm). In addition, microstructure images of machined surfaces were studied by the help of scanning electron microscope (SEM). The results of response surface methodology (RSM) data have been validated by experiment and predictive model. In addition, a multi-target optimisation issue based on the objective functions of an empirical model was used and solved through genetic algorithm (GA). In FG specimens, the RSM with GA was utilised to determine the optimal machining limit value for an exact working condition. Finally, the proposed hard turning strategy using NFMQL method is validated by the help of statistical analysis for huge industries, in particular for the forming sector. The results show that NFMQL method delivered eco-friendly, cleaner manufacturing and enhanced sustainability.

Investigations on Surface Integrity in Hard Turning of Functionally Graded Specimen under Nano Fluid Assisted Minimum Quantity Lubrication