Tribological performance of nanographite-based metalworking fluid and parametric investigation using artificial neural network

This paper investigates the impact of palm oil methyl ester (POME) and graphite nanoflakes as additives on the thermophysical and tribological properties of naphthenic and groundnut oil blends. Different ratios of the two oil blends containing the selected additives were formulated and tested. The results show that both POME and graphite nanoflakes could be potential friction modifiers. Notably, the four-ball tribometer test reveals that the addition of 7 vol% of POME to naphthenic oil and 0.01 wt% graphite nanoflakes to naphthenic oil results in the reduction of wear scar diameter by 24% and 59%, respectively. Surface micrographs of aluminum workpiece subjected to simple turning operation in a CNC machine corroborated the wear test results. POME in naphthenic oil resulted in better surface finish relative to POME-formulated groundnut oil. The experimental data were analyzed using artificial neural network (ANN) to gain a better understanding of the entire work. The sensitivity analysis developed from the chosen ANN models exhibited the nature of influence of the input parameters (groundnut concentrations, POME, and naphthenic oil) on the outputs (Noack volatility, thermal conductivity, and wear).