Deep learning based predictions of Wöhler curve using physics-informed neural networks: self-heating determination of fatigue limits for laminated composite materials
Résumé
This study investigates the fatigue behavior of high-strength carbon/epoxy laminates using Physics-Informed Neural Networks (PINNs). Laminates composed of TR50 unidirectional fibers and R367-2 epoxy were tested under cyclic tension and fatigue at room temperature. Self-heating tests were also conducted to efficiently estimate the fatigue limits. A strong agreement was found between classical fatigue tests and the self-heating method. A PINN-based approach was employed to reconstruct S–N curves by embedding a fatigue model into the learning process. The model, implemented in Python using Keras/TensorFlow, accurately predicts fatigue performance, and a fully commented code example is provided
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