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.

Guest editorial:Special Issue on Artificial Intelligence and Emerging Computational Approaches for Tribology

Tribo-behavior is a complex system-based time-dependent process,and it is difficult to accurately model a tribo-system and predict its behavior.Hence,tribology research,in most cases,has relied on extensive experimentation.Driven by the artificial intelligence(AI)-for-science revolution,AI and other emerging computational approaches provide opportunities to explore the complex processes in tribo-systems and the physical mechanisms of tribo-behavior in an efficient way,significantly pushing the boundaries of tribology research.

Fundamental theories and basic principles of triboelectric effect: A review

Long-term observation of the triboelectric effect has not only proved the feasibility of many novel and useful tribo-devices(e.g., triboelectric nanogenerators), but also constantly motivated the exploration of its mysterious nature. In the pursuit of a comprehensive understanding of how the triboelectric process works, a more accurate description of the triboelectric effect and its related parameters and factors is urgently required. This review critically goes through the fundamental theories and basic principles governing the triboelectric process. By investigating the difference between each charging media, the electron, ion, and material transfer is discussed and the theoretical deduction in the past decades is provided. With the information from the triboelectric series, interesting phenomena including cyclic triboelectric sequence and asymmetric triboelectrification are precisely analyzed. Then, the interaction between the tribo-system and its operational environment is analyzed, and a fundamental description of its effects on the triboelectric process and results is summarized. In brief, this review is expected to provide a strong understanding of the triboelectric effect in a more rigorous mathematical and physical sense.

Multiscale analysis of friction behavior at fretting interfaces

Friction behavior at fretting interfaces is of fundamental interest in tribology and is important in material applications.However,friction has contact intervals,which can accurately determine the friction characteristics of a material;however,this has not been thoroughly investigated.Moreover,the fretting process with regard to different interfacial configurations have also not been systematically evaluated.To bridge these research gaps,molecular dynamics(MD)simulations on Al–Al,diamond–diamond,and diamond–silicon fretting interfaces were performed while considering bidirectional forces.This paper also proposes new energy theories,bonding principles,nanoscale friction laws,and wear rate analyses.With these models,semi-quantitative analyses of coefficient of friction(CoF)were made and simulation outcomes were examined.The results show that the differences in the hardness,stiffness modulus,and the material configuration have a considerable influence on the fretting process.This can potentially lead to the force generated during friction contact intervals along with changes in the CoF.The effect of surface separation can be of great significance in predicting the fretting process,selecting the material,and for optimization.

Metal matrix nanocomposites in tribology: Manufacturing, performance, and mechanisms

Metal matrix nanocomposites(MMNCs)become irreplaceable in tribology industries,due to their supreme mechanical properties and satisfactory tribological behavior.However,due to the dual complexity of MMNC systems and tribological process,the anti-friction and anti-wear mechanisms are unclear,and the subsequent tribological performance prediction and design of MMNCs are not easily possible:A critical up-to-date review is needed for MMNCs in tribology.This review systematically summarized the fabrication,manufacturing,and processing techniques for high-quality MMNC bulk and surface coating materials in tribology.Then,important factors determining the tribological performance(mainly anti-friction evaluation by the coefficient of friction(CoF)and anti-wear assessment with wear rate)in MMNCs have been investigated thoroughly,and the correlations have been analyzed to reveal their potential coupling/synergetic roles of tuning tribological behavior of MMNCs.Most importantly,this review combined the classical metal/alloy friction and wear theories and adapted them to give a(semi-)quantitative description of the detailed mechanisms of improved anti-friction and anti-wear performance in MMNCs.To guarantee the universal applications of these mechanisms,their links with the analyzed influencing factors(e.g.,loading forces)and characteristic features like tribo-film have been clarified.This approach forms a solid basis for understanding,predicting,and engineering MMNCs’tribological behavior,instead of pure phenomenology and experimental observation.Later,the pathway to achieve a broader application for MMNCs in tribo-related fields like smart materials,biomedical devices,energy storage,and electronics has been concisely discussed,with the focus on the potential development of modeling,experimental,and theoretical techniques in MMNCs’tribological processes.In general,this review tries to elucidate the complex tribo-performances of MMNCs in a fundamentally universal yet straightforward way,and the discussion and summary in this review for the tribological performance in MMNCs could become a useful supplementary to and an insightful guidance for the current MMNC tribology study,research,and engineering innovations.

Thermally stable ultrafine grained copper induced by CrB/CrB_(2) microparticles with surface nanofeatures via regular casting

Ultrafine-grained(UFG)/nanocrystalline materials possess novel properties. Refining as-solidified grains of metals to the ultrafine and even nanometer scale by nanoparticles via slow cooling has been recently discovered. Here, we report that microparticles(CrB and CrB_(2)) with surface nanofeatures can also enable ultrafine/nano grains via slow cooling. CrB/CrB_(2) microparticles, formed by coalescence of nanoparticles in Cu matrix, display surface nanofeatures, which induce substantial grain refinement and stabilization down to the ultrafine/nano scale. The UFG Cu/Cr B and Cu/CrB_(2) samples exhibit exceptional thermal stability, comparable to UFG Cu induced by nanoparticles, without coarsening after annealing at 600°C for 1 h. The microhardness, strengths, and Young's moduli of the Cu/Cr B and Cu/CrB_(2) samples are significantly enhanced over pure Cu. This discovery has great potential to advance the mass production UFG/nanocrystalline for widespread applications.