AI for tribology:Present and future

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.

A review of sampling,energy supply and intelligent monitoring for long-term sweat sensors

Sweat is a biofluid with rich information that can reflect an individual’s state of health or activity.But the real-time in situ sweat sensors lack the ability of long-term monitoring.Against this background,this article provides a holistic review on the necessary process and methods for sweat sensing,including sweat collection,composition analysis,energy supply,and data processing.The impacts of the environment in stimulating sweat production,providing energy supply,and intelligent health monitoring are discussed.Based on the review of previous endeavors,the future development in material,structure and artificial intelligence application of long-term sweat monitoring is envisioned.