Thermo-oxidative aging mechanism and machine learning-assisted lifetime prediction of CF/PPESK composites

Key Points

• Lifetime prediction for CF/PPESK composites is effective using machine learning techniques, indicating enhanced reliability.
• Thermo-oxidative aging impacts material properties significantly, leading to degradation observed over time.
• Analysis uses machine learning to model aging mechanisms, offering insight into composite material performance.
• Results may enable better lifecycle predictions for composite materials in various industrial applications.