Physics-informed Neural Network-based Prediction of Multi-factor Coupled Thermal-oxidative Aging Behavior in Polyamide66-Glass Fiber Composites

Key Points

• The predicted aging behavior in polyamide66-glass fiber composites reveals essential dependencies on temperature and oxidative conditions.
• Model outputs indicate notable differences in degradation rates among various composite formulations over time.
• Utilizing physics-informed neural networks provides a novel approach to understand complex material behaviors effectively.
• This method highlights the need for comprehensive modeling tools to predict long-term material performance in real-world applications.