COMPOSITES SCIENCE AND ENGINEERING ›› 2025, Vol. 0 ›› Issue (7): 99-107.DOI: 10.19936/j.cnki.2096-8000.20250728.012

• DESIGN AND TECHNIQUE • Previous Articles     Next Articles

Comparative study of chemorheological properties in hydantoin epoxy resin system using artificial neural networks

LEI Shiyu, LI Ling*, CAO Wei, WANG Yixuan, DONG Xiarui   

  1. School of Material Science and Technology, North University of China, Taiyuan 030051, China
  • Received:2025-03-24 Online:2025-07-28 Published:2025-08-22

Abstract: Chemorheological analysis has been widely applied in the processing of thermoset matrix composites. However, traditional chemorheological models for predicting processing viscosity, such as the Dual-Arrhenius (DA) rheological model, exhibit limited accuracy in thermoset resin systems with multi-field coupling effects. To address this limitation, an artificial intelligence (AI)-based approach was introduced. The reactivity of the hydantoin epoxy resin/maleic anhydride (HY/MAD) system was investigated using differential scanning calorimetry (DSC), and viscosity data were collected under isothermal curing conditions at 65~85 ℃. A backpropagation artificial neural network (BP-ANN) model was developed and systematically compared with the DA model to analyze the rheological behavior of the HY/MAD system. The results demonstrate that the BP-ANN model significantly outperforms the DA model in predictive accuracy: the mean square error is reduced by 26.0%, the mean absolute percentage error decreases sharply by 65.0%, the root mean square error is lowered by 13.0%, and the coefficient of determination improves by 0.25%. This marked enhancement in prediction precision provides critical support for optimizing process parameters and material design in thermoset matrix composite manufacturing, particularly in scenarios involving complex multi-physics coupling.

Key words: hydantoin epoxy resin, Dual-Arrhenius rheological model, backpropagation artificial neural network, chemorheological, viscosity, composites

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