Machine learning and shap-based prediction of multifactor thermal-oxidative aging behavior in glass fiber reinforced polyamide6

In this study, glass fiber-reinforced polyamide 6 (PA6-GF) was selected as a representative system to establish a predictive framework for thermal-oxidative aging behavior. Through nonlinear fitting of traditional empirical models followed by the evaluation of twelve machine learning algorithms, it was found that the conventional models exhibited limited predictive accuracy and generalization capability, whereas the machine learning approaches were able to more effectively capture the complex nonlinear interactions among temperature, oxygen partial pressure, specimen thickness, and aging time. To further elucidate the underlying mechanisms, SHapley Additive exPlanations (SHAP) analysis was employed, highlighting the distinct roles and relative contributions of aging time, oxygen partial pressure, temperature, and thickness in governing the thermal-oxidative aging process. These findings enhance the understanding of multi-factor aging mechanisms and provide practical guidance for improving the long-term durability and reliability of engineering components operating under complex service conditions.