AI for tribology: Present and future

被引:13
|
作者
Yin, Nian [1 ,2 ]
Yang, Pufan [2 ]
Liu, Songkai [2 ]
Pan, Shuaihang [3 ]
Zhang, Zhinan [1 ,2 ]
机构
[1] Shanghai Jiao Tong Univ, State Key Lab Mech Syst & Vibrat, Shanghai, Peoples R China
[2] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200240, Peoples R China
[3] Univ Utah, Dept Mech Engn, Salt Lake City, UT 84112 USA
基金
中国国家自然科学基金;
关键词
artificial intelligence (AI); tribology; machine learning; tribo-informatics; AI for tribology; EXTREME LEARNING-MACHINE; CONVOLUTIONAL NEURAL-NETWORK; TOOL WEAR PREDICTION; ARTIFICIAL-INTELLIGENCE; SURFACE-ROUGHNESS; COMPUTATIONAL INTELLIGENCE; MECHANICAL-PROPERTIES; GENETIC ALGORITHM; HVOF COATINGS; CUTTING FORCE;
D O I
10.1007/s40544-024-0879-2
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
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.
引用
收藏
页码:1060 / 1097
页数:38
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