Deep Learning Applications in the Analysis of Wear Mechanisms in Metallic Materials: A Review

The accurate diagnosis and prediction of metal wear mechanisms are critical for ensuring the durability and safety of industrial systems. Conventional tribological methods, although widely used, are limited in processing complex data sets, identifying early stage wear features, and enabling real-time monitoring. Recently, deep learning has shown strong potential to address these challenges through automated feature extraction, better temporal prediction, and more accurate anomaly detection. Particularly, models such as the Convolutional neural network, Long Short-Term Memory network, and Autoencoder have been successfully applied to image-based surface analysis, vibration and acoustic signal interpretation, and wear progression forecasting. Advanced hybrid architectures and cross-domain transfer techniques further extend the applicability of deep learning to multimodal wear analysis under diverse operating conditions. This Review systematically explores the current advancements in deep learning-driven wear analysis, critically assessing model capabilities, data dependencies, and physical interpretability, and outlines future directions for integrating AI systems into tribological research and industrial diagnostics.