Accelerated aging tests are widely used to rapidly evaluate the durability of materials,of which thermal-oxidative aging is the most common approach.To quantitatively predict the effects of multiple coupled factors,th...Accelerated aging tests are widely used to rapidly evaluate the durability of materials,of which thermal-oxidative aging is the most common approach.To quantitatively predict the effects of multiple coupled factors,this study takes polyamide66 reinforced with glass fiber(PA66-GF)as a model system and proposed a high-precision paradigm for coupled thermal-oxidative aging.By integrating Arrhenius-type reaction kinetics with oxygen diffusion,a predictive formula that holistically captures the nonlinear synergistic effects of multiple factors was developed,thereby overcoming the limitations of traditional single-variable models.A systematic evaluation of the stepwise improved formulas through nonlinear fitting showed that the coefficient of determination(R^(2))increased from 0.223 to 0.803,elucidating the fundamental reason why conventional approaches fail in quantitative prediction.These formulae were further embedded as physical constraints into a physics-informed neural network(PINN),which further enhanced the predictive performance,with the proposed formula achieving a peak R^(2)of 0.946.The results highlight that robust data fitting alone is insufficient;the decisive factor for the success of PINN lies in whether the embedded formula faithfully reflects the underlying physical mechanisms.When applied to polyamide 6 reinforced with glass fiber(PA6-GF),the Formula-constrained PINN maintained a high level of accuracy(R^(2)=0.916),demonstrating its strong cross-system generalizability.In summary,this work establishes a robust hybrid physics-machine learning framework that combines high accuracy with transferability for predicting the thermal-oxidative aging behavior of composite material systems.展开更多
基金financially supported by the National Natural Science Foundation of China(No.22473032)。
文摘Accelerated aging tests are widely used to rapidly evaluate the durability of materials,of which thermal-oxidative aging is the most common approach.To quantitatively predict the effects of multiple coupled factors,this study takes polyamide66 reinforced with glass fiber(PA66-GF)as a model system and proposed a high-precision paradigm for coupled thermal-oxidative aging.By integrating Arrhenius-type reaction kinetics with oxygen diffusion,a predictive formula that holistically captures the nonlinear synergistic effects of multiple factors was developed,thereby overcoming the limitations of traditional single-variable models.A systematic evaluation of the stepwise improved formulas through nonlinear fitting showed that the coefficient of determination(R^(2))increased from 0.223 to 0.803,elucidating the fundamental reason why conventional approaches fail in quantitative prediction.These formulae were further embedded as physical constraints into a physics-informed neural network(PINN),which further enhanced the predictive performance,with the proposed formula achieving a peak R^(2)of 0.946.The results highlight that robust data fitting alone is insufficient;the decisive factor for the success of PINN lies in whether the embedded formula faithfully reflects the underlying physical mechanisms.When applied to polyamide 6 reinforced with glass fiber(PA6-GF),the Formula-constrained PINN maintained a high level of accuracy(R^(2)=0.916),demonstrating its strong cross-system generalizability.In summary,this work establishes a robust hybrid physics-machine learning framework that combines high accuracy with transferability for predicting the thermal-oxidative aging behavior of composite material systems.
