This study presents a baseline-free, self-supervised framework for defect detection in carbon fiber–reinforced polymer (CFRP) laminates using laser ultrasonic guided wave (LUGW) wavefield data. The experimental investigation involves two quasi-isotropic eight-ply laminates with a 0/45/90/−45 S stacking sequence, each containing an artificial defect made by a centrally embedded square PTFE insert (10×10 mm 2 and 5×5 mm 2 ) at the mid-plane to simulate local delamination. Data preprocessing includes narrowband temporal filtering centered on the dominant carrier frequency and frame-wise spatial detrending, followed by tri-planar decomposition of the spatiotemporal wavefield volume. Each orthogonal plane is processed independently using attention-assisted blind-spot U-Nets trained through masked-pixel restoration, producing per-pixel posterior means and log-variances. The three probabilistic estimates are fused using a product-of-experts (PoE) model, yielding a unified prediction with calibrated uncertainty. A standardized residual between the observed data and the fused prediction is used to drive adaptive thresholding and morphological post-processing, resulting in coherent delamination masks. The uncertainty-aware fusion strategy effectively down-weights low-information regions and mitigates boundary artifacts. A dual decision criterion based on the area fraction and the mean anomaly score enables robust detection. The proposed pipeline achieves accurate and interpretable delamination localization without reliance on prior knowledge of structural properties, defect presence, baseline measurements, or labeled training data.
Azadi et al. (2026) studied this question.