Machine Learning in Wear Prediction

Abstract

As modern devices and systems continue to advance, device wear remains a key factor in limiting their performance and lifetime, as well as environmental and health effects. Traditional approaches often rely on wear prediction based on physical models, but due to device complexity and uncertainty, these methods often fail to provide accurate predictions and accurate wear identification. Machine learning, as a data-driven approach based on its ability to discover patterns and correlations in complex systems, has enormous potential for monitoring and predicting device wear. Here, we review recent advances in applying machine learning for predicting the wear of mechanical components. Machine learning for wear prediction shows significant potential in optimizing material selection, manufacturing processes, and equipment maintenance, ultimately enhancing productivity and resource efficiency. Successful implementation relies on careful data collection, standardized evaluation methods, and the selection of effective algorithms, with artificial neural networks (ANNs) frequently demonstrating notable success in predictive accuracy.