Progress on current-carrying friction and wear and prediction models: A review

Current-carrying friction pairs are extensively utilized in industries such as electrified railways, aerospace, energy, and other fields because of their exceptional energy transmission efficiency and reliability. With the operating conditions and environment of current-carrying friction pairs becoming increasingly extreme, the mechanical–electrical coupling effects within these systems have intensified, resulting in frequent system failures, significantly shortened service lifespans, and even threats to operational safety. Therefore, investigating the wear mechanisms and characteristics of current-carrying friction pairs and developing predictive models are critical. This work comprehensively reviews the wear theories and prediction models pertinent to current-carrying tribo-pairs, summarizing their fundamental features and tribological behaviors. The influence of variables such as current, velocity, load, and environmental conditions on wear characteristics is systematically examined, highlighting the importance of arc erosion and the interplay of multiple factors. Existing prediction models are categorized into mechanistic models, numerical simulation models, and artificial intelligence models, with a detailed overview of the progress in each model. These models correlate various parameters with tribological properties, enabling fast and accurate evaluation and prediction of wear characteristics. However, these applications require specific conditions such as material properties, tribo-pair types, or operational environments. Notably, the predictive capabilities of artificial intelligence methods, including machine learning and deep learning, remain highly contingent on data quality. Finally, this work concludes by identifying current challenges in the research of current-carrying friction and wear, offering recommendations for enhancements to advance understanding in the field of current-carrying tribology, and providing valuable insights for future research efforts.