@misc{10481/107920,
year = {2026},
month = {1},
url = {https://hdl.handle.net/10481/107920},
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.},
organization = {European Union (Horizon 2020, Marie Skłodowska-Curie grant 859957)},
publisher = {Elsevier},
keywords = {Composite laminates},
keywords = {Fatigue delamination},
keywords = {Shape prognostics},
title = {Fatigue delamination shape prognostics in composites using numerical simulation-assisted transfer learning},
doi = {10.1016/j.aei.2025.104025},
author = {Zhong, Ruirui and Wang, Xi Vicent and Wang, Lihui and Chiachío Ruano, Manuel and Cadini, Francesco and Sbarufatti, Claudio and Li, Tianzhi},
}