Tribology, Artificial Intelligence, Machine Learning: Multicomponent Interaction (Review)
DOI:
https://doi.org/10.52171/herald.416Keywords:
tribology, artificial intelligence (AI), machine learning (ML), “green” tribology, triboinformatics, artificial neural network (ANN)Abstract
This article presents a review analysis and develops a process scheme in the field of tribology related to the application and development of artificial intelligence, on the basis of which a systematic approach has been carried out. It clearly demonstrates that one of the fundamental characteristics of tribology is the strong interconnection between different scientific disciplines. The conducted analysis shows that tribology is not only a complex mechanical system but also a research field that inevitably requires interdisciplinary efforts. The study indicates that progress in tribology is increasingly associated with the development of artificial intelligence concepts, machine learning, triboinformatics, and “green” tribology. From this perspective, the article emphasizes that future tribologists should focus on advancing scientific fields such as mechanical engineering, physics, and materials science within the framework of next-generation information and communication technologies. Artificial intelligence (AI), with its advanced machine learning (ML) capabilities and the rapid processing capacity of triboinformatics, can help researchers quickly identify valuable patterns, trends, and relationships from subjective and experimental data. Tribological behavior depends on the system, changes over time, and is characterized by complex multidisciplinary interactions. The friction process involves various physical phenomena including mechanics, thermology, electricity, optics, magnetism, and others. Therefore, tribological data are characterized by complex multicomponent interactions and possess features such as multidisciplinarity, multilevel structure, and multiscale nature. Considering the emergence of triboinformatics, a new interdisciplinary field formed by the integration of tribology and informatics, this review expands the future research directions and framework of the concept of “Artificial Intelligence for Tribology.” As a result of extensive research, a scientifically substantiated new roadmap (framework) has been developed for the application and advancement of artificial intelligence and machine learning in the field of tribology. By establishing a systematic understanding, it is recommended to enhance the efficiency of solving problems in tribology. Such an approach will enable the application of triboinformatics methods to address common problems such as monitoring tribological behavior, predicting system performance, and optimizing system operation.
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