Tribo-Informatics: The Systematic Fusion of AI and Tribology

This book details how artificial intelligence and other informatic methods can be applied to the field of tribology. Using problems often found within tribological condition monitoring, behaviour prediction, system optimization and mechanism analysis, the book covers methods used such as Artificial Neural Networks (ANN).

Including case studies throughout, the book offers an accessible introduction to tribological research, beginning with background on the theory behind tribo-informatics, and updates in the latest technology. It describes how to establish a tribo-informatics database, methods through which to express tribo-systems such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF), and its applications. It can be used in state monitoring, behaviour prediction and system optimization. Through case studies, practical examples of how tribo-informatics can be implemented are shown throughout various industries.

This book will be of interest to students and researchers in the field of tribology, friction, wear and artificial intelligence.

Zhinan Zhang

He received his Ph.D. in Shanghai Jiao Tong University. From 2011 to 2013, he worked as a Postdoc in Shanghai Jiao Tong University. He is currently a professor at the School of Mechanical Engineering, Shanghai Jiao Tong University. His research interests include tribo-informatics, contact electrification, computational design, and analysis of tribo-systems.

Nian Yin

He received his B.S. degree and M.D. in mechanical engineering in 2017 and 2020, respectively, from Shanghai Jiao Tong University, Shanghai, China. He is now working as a Ph.D. student at the School of Mechanical Engineering, Shanghai Jiao Tong University. His research focuses on the computational design of materials, contact electrification, and development of tribology testing machine