Data-driven modeling for tribological performance of nano-lubricants using artificial neural network

There is an upsurge in demand for newer lubricants which effectively can reduce friction, protect contact surfaces from wear, corrosion, oxidation, and provide thermal stability. The present study addresses the need of expediting the research with the use of Artificial Intelligence and data-driven modeling. This article aimed at prediction of tribological performance of lubricant with addition of nanoparticles (NP) additives using data available in the literature. A data-driven modeling is used to find the relationships between input and output variables of a system without knowing the physical behavior of the system. In this study key attributes (input) are identified and tribological performance parameters (output) are specified. The relationship between the inputs and outputs is established by correlation analysis and artificial neural network (ANN) model.