Interpretable Computer Vision for Defect Detection in X-ray Tomography of Aerospace SiC/SiC Composites
Researchers have developed p-ResNet-50, a prototype-based convolutional architecture that marries high-accuracy defect detection with human-interpretable explanations for aerospace composite inspection. Rather than treating deep learning as a black box, the model grounds its classifications in six learned prototypes aligned to expert-defined defect categories, making accept/reject decisions traceable and auditable. This work signals a maturing shift in industrial AI: moving beyond raw accuracy to coupling automation with the transparency and accountability that regulated manufacturing demands. The approach has implications beyond composites for any high-stakes inspection domain where regulatory bodies or customers require explainability alongside performance.58
