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Friction 2024, 12(6): 1060-1097 https://doi.org/10.1007/s40544-024-0879-2
Review Article | Open Access | Issue | Published: 12 March 2024

AI for tribology: Present and future

Show Author's Information Nian YIN1,2,  Pufan YANG2,  Songkai LIU2,  Shuaihang PAN3,  Zhinan ZHANG1,2( )
State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Department of Mechanical Engineering, University of Utah, Salt Lake City, UT 84112, USA
Keywords:
machine learning, tribology, artificial intelligence (AI), tribo-informatics, AI for tribology
Cite this article:
YIN N, YANG P, LIU S, et al. AI for tribology: Present and future. Friction, 2024, 12(6): 1060-1097. https://doi.org/10.1007/s40544-024-0879-2
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Abstract Full text About this article

With remarkable learning capabilities and swift operational speeds, artificial intelligence (AI) can assist researchers in swiftly extracting valuable patterns, trends, and associations from subjective information. Tribological behaviors are characterized by dependence on systems, evolution with time, and multidisciplinary coupling. The friction process involves a variety of phenomena, including mechanics, thermology, electricity, optics, magnetics, and so on. Hence, tribological information possesses the distinct characteristics of being multidisciplinary, multilevel, and multiscale, so that the application of AI in tribology is highly extensive. To delineate the scope, classification, and recent trends of AI implementation in tribology, this review embarks on exploration of the tribology research domain. It comprehensively outlines the utilization of AI in basic theory of tribology, intelligent tribology, component tribology, extreme tribology, bio-tribology, green tribology, and other fields. Finally, considering the emergence of "tribo-informatics" as a novel interdisciplinary field, which combines tribology with informatics, this review elucidates the future directions and research framework of "AI for tribology". In this paper, tribo-system information is divided into 5 categories: input information (I), system intrinsic information (S), output information (O), tribological state information (Ts), and derived state information (Ds). Then, a fusion method among 5 types of tribo-system information and different AI technologies (regression, classification, clustering, and dimension reduction) has been proposed, which enables tribo-informatics methods to solve common problems such as tribological behavior state monitoring, behavior prediction, and system optimization. The purpose of this review is to offer a systematic comprehension of tribo-informatics and to inspire new research ideas of tribo-informatics. Ultimately, it aspires to enhance the efficiency of problem-solving in tribology.


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About this article

AI for tribology: Present and future

Show Author's information Nian YIN1,2, Pufan YANG2, Songkai LIU2, Shuaihang PAN3, Zhinan ZHANG1,2( )
State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Department of Mechanical Engineering, University of Utah, Salt Lake City, UT 84112, USA

Abstract

With remarkable learning capabilities and swift operational speeds, artificial intelligence (AI) can assist researchers in swiftly extracting valuable patterns, trends, and associations from subjective information. Tribological behaviors are characterized by dependence on systems, evolution with time, and multidisciplinary coupling. The friction process involves a variety of phenomena, including mechanics, thermology, electricity, optics, magnetics, and so on. Hence, tribological information possesses the distinct characteristics of being multidisciplinary, multilevel, and multiscale, so that the application of AI in tribology is highly extensive. To delineate the scope, classification, and recent trends of AI implementation in tribology, this review embarks on exploration of the tribology research domain. It comprehensively outlines the utilization of AI in basic theory of tribology, intelligent tribology, component tribology, extreme tribology, bio-tribology, green tribology, and other fields. Finally, considering the emergence of "tribo-informatics" as a novel interdisciplinary field, which combines tribology with informatics, this review elucidates the future directions and research framework of "AI for tribology". In this paper, tribo-system information is divided into 5 categories: input information (I), system intrinsic information (S), output information (O), tribological state information (Ts), and derived state information (Ds). Then, a fusion method among 5 types of tribo-system information and different AI technologies (regression, classification, clustering, and dimension reduction) has been proposed, which enables tribo-informatics methods to solve common problems such as tribological behavior state monitoring, behavior prediction, and system optimization. The purpose of this review is to offer a systematic comprehension of tribo-informatics and to inspire new research ideas of tribo-informatics. Ultimately, it aspires to enhance the efficiency of problem-solving in tribology.

Keywords: machine learning, tribology, artificial intelligence (AI), tribo-informatics, AI for tribology

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Publication history
Copyright
Acknowledgements
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Publication history

Received: 13 July 2023
Revised: 13 November 2023
Accepted: 29 January 2024
Published: 12 March 2024
Issue date: June 2024

Copyright

© The author(s) 2024.

Acknowledgements

This study is financially supported by the National Natural Science Foundation of China (Grant Nos. 12072191, 51875343, and 51575340), State Key Laboratory of Mechanical System and Vibration Project (Grant Nos. MSVZD202108, MSVZD201912), and the Shanghai Academy of Space Technology-Shanghai Jiao Tong University Joint Research Center of Advanced Aerospace Technology (Grant Nos. USCAST2020-36, USCAST2022-15).

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