Fatigue delamination shape prognostics in composites using numerical simulation-assisted transfer learning

Abstract

Delamination shape holds crucial information for evaluating structural safety, including its area, center, and perimeter; thus, shape prognostics has recently gained significant attention using either numerical simulations or data-driven models. Numerical approaches can capture the general trend of delamination growth while failing to account for the uncertainties arising from experimental or in-field fatigue damage growth processes. Both simulations and experiments show delamination growth along the same primary direction, but experimental observations exhibit a high degree of stochasticity in their growth rates and shape evolution that simulations cannot capture. Data-driven methods are capable of describing the actual fatigue behavior, while requiring a substantial experimental database for training. To bridge the gap between numerical simulations and complex experimental realities, we propose a framework that integrates delamination growth simulations with a data-driven approach to predict the evolution of fatigue delamination shapes. It first utilizes numerical data to train a neural ordinary differential equation (ODE)-based model that learns the gradient of the shape evolution. Subsequently, a progressive transfer learning strategy is then employed to incrementally refine the learned model using experimental observations during fatigue loading, overcoming the inherent limitations of conventional data fusion methods and enabling robust prognostics. The effectiveness of the proposed approach is demonstrated using experimental composite fatigue tests with ultrasonic C-scan monitoring, showing consistent improvement in prognostic accuracy compared with simulation-only, experiment-only, and mixed training strategies.