Tribo-informatics approaches in tribology research: A review

Abstract

Tribology research mainly focuses on the friction, wear, and lubrication between interacting surfaces. With the continuous increase in the industrialization of human society, tribology research objects have become increasingly extensive. Tribology research methods have also gone through the stages of empirical science based on phenomena, theoretical science based on models, and computational science based on simulations. Tribology research has a strong engineering background. Owing to the intense coupling characteristics of tribology, tribological information includes subject information related to mathematics, physics, chemistry, materials, machinery, etc. Constantly emerging data and models are the basis for the development of tribology. The development of information technology has provided new and more efficient methods for generating, collecting, processing, and analyzing tribological data. As a result, the concept of “tribo-informatics (triboinformatics)” has been introduced. In this paper, guided by the framework of tribo-informatics, the application of tribo-informatics methods in tribology is reviewed. This article aims to provide helpful guidance for efficient and scientific tribology research using tribo-informatics approaches.

A data-driven approach for studying tribology based on experimentation and artificial intelligence coupling tools

“…It takes into account the different information from data sources to select the most influenced parameters. This framework was tested by [22,23] and reported by [24,25]. and predict the most influenced parameters…”

Effect of Normal Load on the Nanofriction Behavior of Graphene on Stainless-Steel Substrate: Implication for Nanoscale Lubrication

“…Stainless steel is a commonly used basic material in a mechanical structure, − but its wear resistance is poor . And in the process of mechanical structure movement, friction wear has always been the main reason for the stability and life of the mechanical structure. − With the development of the grain refinement process, ultrastrong nanocrystalline stainless steel with better comprehensive performance can be prepared, which makes stainless steel also become a potential basic material in micromechanical structures. However, in microelectromechanical systems (MEMS) and micromechanical structures, the impact of frictional wear on system stability will be more serious. − Reducing the frictional wear of superstrong nanocrystalline stainless steel in nanofriction becomes the key to further application of stainless-steel materials in micromechanics.…”

GNBoost-Based Ensemble Machine Learning for Predicting Tribological Properties of Liquid-Crystal Lubricants

“…Machine learning offers clear advantages in this field, with a growing body of research focused on its application in predicting the friction-reducing capability and wear of lubricating materials. Yin et al 17 outlined the application of machine learning and artificial intelligence in tribology. They introduced the performances of four methods, ANN, SVM, KNN, and RF, in tasks such as regression, classification, clustering, and dimensionality reduction.…”