Creep Prediction of Fluoroplastics Based on Physics Informed Neural Networks

Fluoroplastics are key sealing materials in rocket engines. They can easily fail due to creep during long-term storage, which may threaten the engines’ safety during operation. Accurate prediction of their time-dependent deformation under varying temperature and pressure conditions remains a critical challenge. Existing methods for predicting polymer creep, such as empirical methods and physically-based constitutive models, often depend on fixed working conditions and require material composition analysis. To address these issues, this paper proposes a physics-informed two-stage framework for predicting the extrapolative creep behavior of fluoroplastics. An Extended Burgers Model (EBM) with adaptive parameters is employed to represent viscoelastic behavior under coupled temperature–pressure variations. A hybrid PSO–NLS strategy is developed to robustly identify EBM parameters from 18 experimental creep curves, overcoming initialization sensitivity and ensuring high-quality training labels. Subsequently, a neural network learns the mapping from service conditions (T,F) to EBM parameters, guided by material-specific physical constraints that enforce long-term consistency and extrapolation capabilities. The resulting model generates full creep strain curves ε(t) that are both data-driven and physically plausible. Experimental results demonstrate that the proposed method surpasses conventional empirical models in extrapolation accuracy, adherence to physical principles, and ease of practical implementation. It combines the strong fitting ability of data-driven models with the interpretability of physics-based models to improve both prediction accuracy and generalization performance.