Machine Learning in Wear Prediction

As modern devices and systems continue to advance, device wear remains a key factor inlimiting their performance and lifetime, as well as environmental and health effects. Tradi-tional approaches often rely on wear prediction based on physical models, but due to devicecomplexity and uncertainty, these methods often fail to provide accurate predictions andaccurate wear identification. Machine learning, as a data-driven approach based on itsability to discover patterns and correlations in complex systems, has enormous potentialfor monitoring and predicting device wear. Here, we review recent advances in applyingmachine learning for predicting the wear of mechanical components. Machine learningfor wear prediction shows significant potential in optimizing material selection, manufac-turing processes, and equipment maintenance, ultimately enhancing productivity andresource efficiency. Successful implementation relies on careful data collection, standard-ized evaluation methods, and the selection of effective algorithms, with artificial neural net-works (ANNs) frequently demonstrating notable success in predictive accuracy