Prior information-aided severity-graded fatigue delamination prognostics in composites

Fatigue prognostic studies in composites often model the delamination evolution over time in one or two dimensions, for example, by depicting one-dimensional (1D) length or two-dimensional (2D) shape contour. However, these representations generally provide limited information about the spatial variation in damage severity within the delaminated region. In this context, this work proposes a prognostic framework for severity-graded fatigue delamination maps in composite laminates. Firstly, to facilitate downstream prognostic modeling, ultrasonic C-scan images are processed through pixel-intensity-based discretization, where pixel intensity values are transformed into discrete damage severity levels and replaced with integer class labels, resulting in severity-graded feature maps that simultaneously characterize the spatial extent and severity distribution of delamination-related damage. Then, a U-Net–transformer model is trained with prior-information-aided loss functions and employed to perform long-horizon prognostics of future severity-graded delamination maps. The framework is demonstrated through fatigue experiments on composite laminates monitored using ultrasonic C-scan inspections. The codes and data are publicly available at https://github.com/JingYuan93/3D-delamination-prognostics .