Journal
ENGINEERING FRACTURE MECHANICS
Volume 289, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engfracmech.2023.109456
Keywords
Fatigue life prediction; Multiaxial fatigue; Physics-informed neural network; Sensitive features
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This paper proposes a physics-informed neural network framework to improve the accuracy of multiaxial fatigue life prediction.
Deep learning is a widely used tool for multiaxial fatigue life prediction. However, neural network still needs to solve various problems in the solution dominated by physical equations. This work proposes a physics-informed neural network framework incorporating sensitive features and life prediction models. The framework enhances the training process of the neural network under the constraint of life prediction model. Sensitive features are extracted to select input features with higher importance to the model output. The results show that the filtered sensitive features improve the predictive performance of the neural network. Introducing the Smith-Watson-Topper model as a physical loss degrades the predictive performance of the neural network. On the contrary, introducing Fatemi-Socie and Shang-Wang model as physical loss improves the predictive performance of neural network.
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