The hybrid model of domain knowledge, symbolic regression and neural networks for multiaxial fatigue life prediction

To further enhance the prediction performance of multiaxial fatigue life based on Physics Informed Neural Network (PINN), Domain Knowledge guided Symbolic Regression-Neural Network (DKSR-NN) framework is proposed. At first, domain knowledge is employed to guide symbolic regression in generating expressions that possess both physical interpretability and high predictive accuracy. These expressions are then incorporated as physics-informed loss functions within the PINN architecture. This integration significantly improves the model’s accuracy and stability, especially under conditions of limited fatigue data. At last, the proposed method is validated by comparisons with different machine learning on AZ61A magnesium alloy, TC4 titanium alloy, and sintered porous iron. The results demonstrate that the DKSR-NN framework is better than PINN using critical plane models as physical loss constraints, DKSR and pure data-driven machine learning methods. This will provide a prospect for multiaxial fatigue life prediction.