A Dual-Physics-Informed Neural Network with Incremental Learning for Corrosion Fatigue Crack Growth Prediction in Aluminum Alloys

Aluminum alloys used in aircraft structures are susceptible to corrosion fatigue cracking under combined aggressive environments and cyclic loading, threatening structural integrity. Pure data-driven models often fail under distribution shifts, while single-physics-informed neural networks (PINNs) lack flexibility in complex conditions. This paper proposes a dual-physics-informed neural network (DPINN) that integrates Walker and Forman crack growth models into a deep residual network. The model adaptively fuses both physical formulas via a trainable weight α and predicts material constants. A hybrid loss function with α regularization ensures physically consistent predictions. Using comprehensive corrosion fatigue data covering eight aluminum alloys, we evaluate the model on an internal test set and, more importantly, on an independent external test set simulating real-world distribution shifts. We further investigate an incremental learning scenario where the model is sequentially fine-tuned with increasing fractions of the external set. Results demonstrate that DPINN rapidly rectifies initial distribution mismatch, crossing the engineering reliability threshold (R2 > 0.90) at an early incremental stage, and achieves superior performance after fine-tuning, significantly outperforming both a single Walker-PINN and gradient boosting regressors. SHAP feature importance analysis identifies ΔK and stress ratio as dominant drivers, confirming mechanistic consistency. The proposed architecture offers a data-efficient and interpretable tool for corrosion fatigue crack growth prediction in aluminum alloy structures.