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

Hui Zhan; Jie Liu; Sen-Hua Zhan; Bo Wu; Tong-Fei Shi

doi:10.1007/s10118-025-3509-1

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

RESEARCH ARTICLE | Updated：2026-01-30

- Physics-informed Neural Network-based Prediction of Multi-factor Coupled Thermal-oxidative Aging Behavior in Polyamide66-Glass Fiber Composites
- Chinese Journal of Polymer Science Vol. 44, Issue 2, Pages: 437-449(2026)
- Affiliations：
  
  a.School of Chemical Engineering and Light Industry, Guangdong University of Technology, Guangzhou 510006, China
  b.Enterprise Technology Center, Kingfa Science & Technology Co., Ltd., Guangzhou 510663, China
  c.National Industrial Innovation Center of Polymer Materials, Guangzhou 510663, China
- Author bio：
  
  admin@guogaocai.com (B.W.)
  tfshi@gdut.edu.cn (T.F.S.)
- Funds：
- DOI：10.1007/s10118-025-3509-1
  CLC：
- Received：16 October 2025，
  
  Accepted：18 November 2025，
  
  Online First：22 January 2026，
  
  Published：05 February 2026
- Accepted：
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Zhan, H.; Liu, J.; Zhan, S. H.; Wu, B.; Shi, T. F. Physics-informed neural network-based prediction of multi-factor coupled thermal-oxidative aging behavior in polyamide66-glass fiber composites. Chinese J. Polym. Sci. 2026, 44, 437–449

Hui Zhan, Jie Liu, Sen-Hua Zhan, et al. Physics-informed Neural Network-based Prediction of Multi-factor Coupled Thermal-oxidative Aging Behavior in Polyamide66-Glass Fiber Composites[J]. Chinese Journal of Polymer Science, 2026, 44(2): 437-449.
Zhan, H.; Liu, J.; Zhan, S. H.; Wu, B.; Shi, T. F. Physics-informed neural network-based prediction of multi-factor coupled thermal-oxidative aging behavior in polyamide66-glass fiber composites. Chinese J. Polym. Sci. 2026, 44, 437–449 DOI： 10.1007/s10118-025-3509-1.

Hui Zhan, Jie Liu, Sen-Hua Zhan, et al. Physics-informed Neural Network-based Prediction of Multi-factor Coupled Thermal-oxidative Aging Behavior in Polyamide66-Glass Fiber Composites[J]. Chinese Journal of Polymer Science, 2026, 44(2): 437-449. DOI： 10.1007/s10118-025-3509-1.

摘要

This work presents a physics-informed neural network (PINN)-based method for predicting the thermal-oxidative aging of polyamide composites

in which a diffusion-reaction coupled formula is embedded as a soft constraint to ensure physical consistency under multifactor coupling and to enable cross-system generalization.

Abstract

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 reac

tion 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 (

) 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

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 (

=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.

关键词

Keywords

references

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