A comprehensive review of computational methods for predicting tribological behavior in agricultural machinery components

Tribological phenomena such as wear, friction, and lubrication critically influence the performance and durability of agricultural machinery components. This paper presents a comprehensive review of various computational methods employed for predicting tribological behavior in agricultural equipment. The surveyed techniques include mathematical modeling, finite element analysis (FEA), discrete element method (DEM), computational fluid dynamics (CFD), and artificial intelligence (AI)-based approaches such as artificial neural networks (ANN) and machine learning (ML). The review summarizes key findings, advantages, and limitations of each method, highlighting their applicability to different components and operating conditions. Additionally, the paper discusses future research directions including green tribology and continuous damage mechanics, aiming to support sustainable and efficient agricultural machinery design. This work serves as a valuable resource for researchers and engineers seeking advanced predictive tools in agricultural tribology.