Artificial Intelligence-Based Rapid Design of Grease with Chemically Functionalized Graphene and Carbon Nanotubes as Lubrication Additives.

Rapid chemical functionalization of additives and efficient determination of their optimum concentrations are important for designing high-performance lubricants, especially under multi-additive conditions. Herein, chemically functionalized graphene (FGR) and carbon nanotubes (FCNTs) were rapidly prepared by microwave-assisted ball milling and subsequently introduced into grease as additives. The tribological properties of the additives in grease at different concentrations and ratios were measured using a four-ball test. A reliable artificial neural network (ANN) model was established according to a few test results. Subsequently, the optimal concentration of multiple additives in the grease was predicted using a genetic algorithm and experimentally validated. The results indicated that the introduction of FGR (0.14 wt %) and FCNT (0.16 wt %) improved the antifriction and anti-wear performance of the base grease by 25.66 and 29.34%, respectively. The results of the ANN model analysis and friction interface characterization indicate that such performance is principally attributed to the synergistic lubrication of the FGR and FCNT.