Abstract—The objective of this study is to investigate and predict wear behavior in contacting surfaces through an integrated approach that combines Continuum Damage Mechanics (CDM) and Artificial Neural Networks (ANN). Pin on disk wear experiments were performed under dry conditions using three engineering materials ST37, C45E4, and Al7075 with variations in load, sliding speed, and hardness systematically designed using a full factorial Design of Experiments (DOE). The CDM model quantified material degradation and estimated wear coefficients, which were then used as training data for a feed-forward back-propagation ANN. Both models were validated against independent experimental data. Results indicate that the ANN model achieved high prediction accuracy (average error<5%), outperforming the CDM model (average error ≤12%). Scanning Electron Microscopy (SEM) revealed adhesive wear as dominant in the steels, while Al7075 exhibited reduced wear due to higher hardness. The interaction effects showed that load and sliding speed have significant influences on wear, whereas hardness plays a secondary role. The findings establish a robust framework for wear prediction, process optimization, and potential real-time monitoring in engineering applications, demonstrating the effective integration of physics-based and data-driven modeling in predictive tribology.

Keywords—tribology, surface wear, Continuum Damage Mechanics (CDM), steady-state, Artificial Neural Network (ANN)

Cite: Sahar Ghatrehsamani, Mohammad Silani, Saleh Akbarzadeh, and Shirin Ghatrehsamani, "Prediction of Wear Behavior in Surface Contact Using CDM and ANN Models," International Journal of Mechanical Engineering and Robotics Research, Vol. 15, No. 2, pp. 135-144, 2026. doi: 10.18178/ijmerr.15.2.135-144

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).