Surface degradation governs the durability and reliability of engineering components operating under combined mechanical and chemical loading. In many practical systems, corrosion and wear occur simultaneously at material interfaces, yet they are still frequently modelled as independent degradation modes. This separation limits predictive capability and obscures the coupled nature of electrochemical and tribological damage evolution. In this work, an identifiable, physics-governed digital twin is developed to model surface degradation arising from coupled corrosion and wear within a unified framework. The approach introduces a single internal surface aging state that represents cumulative and irreversible interfacial damage. This state evolves according to physically motivated degradation kinetics and consistently governs both corrosion current and friction coefficient responses. By embedding the governing evolution law directly into the learning formulation, the digital twin enforces monotonicity, irreversibility, and boundedness of degradation, thereby preventing non-physical predictions while preserving structural identifiability of model parameters. Systematic sensitivity analysis quantifies the influence of degradation rate and response coupling parameters on predicted surface behaviour. Robustness of the inferred aging dynamics is further demonstrated through five-fold cross-validation and noise perturbation studies up to 20% measurement disturbance, confirming stable and repeatable parameter identification. Comparison with classical empirical degradation models highlights the improved physical admissibility and extrapolation behaviour of the proposed framework. Overall, the physics-governed digital twin provides a unified, interpretable, and data-efficient modelling foundation for predictive surface engineering applications involving corrosion-resistant materials, tribological interfaces, and functional coatings.
Aswin Karkadakattil (Mon,) studied this question.