POSSIBILITIES OF APPLYING ARTIFICIAL INTELLIGENCE IN THE FIELD OF TRIBOLOGICAL RESEARCH

Abstract

Tribological behavior is a complex, time-dependent process influenced by multiple factors, making it difficult to precisely model and predict the performance of tribo-systems. Tribology research has relied on labor-intensive experimental methods to understand these intricate mechanisms. However, the advancements in artificial intelligence (AI) and machine learning (ML) have introduced new methods for analyzing and interpreting complex tribological processes with greater accuracy and efficiency. The integration of AI into tribology has led to the development of "tribo-informatics," a field that merges tribological data with computational techniques to perform predictions and system optimization. ML models, such as neural networks (NN), contrastive learning frameworks, and Bayesian inference methods, have demonstrated remarkable improvements in wear prediction, lubrication analysis, and failure diagnostics. Furthermore, computational approaches such as physics-informed neural networks (PINNs) have enabled more precise modeling of fundamental tribological equations, improving the understanding of surface interactions and material wear mechanisms. This paper examines the potential of AI in tribology, showcasing how modern computational tools are driving innovations in wear assessment, lubricant performance analysis, and the design of advanced tribological materials. The findings highlight the growing role of AI in optimizing tribological performance and advancing the predictive capabilities of tribology research