Non-destructive testing of aerospace SiC/SiC composites via X-ray computed tomography (XCT) relies on expert visual assessment, with current workflows offering limited traceability for accept/reject decisions. Deep convolutional networks can automate defect detection, yet their black-box nature conflicts with the transparency that industrial inspection practice demands. To close this gap, we introduce p-ResNet-50, a convolutional framework extended with a prototype layer that couples high detection accuracy with case-based explanations. Six learned prototypes are explicitly aligned with expert-defined semantic categories-healthy matrix, matrix--air interfaces, pores, line-like defects, and mixed morphologies-so that every classification is traceable to a physically meaningful reference. Two novel regularisation terms, anchor-based and medoid-based, tether prototypes to expert-selected patches and prevent prototype collapse, addressing a known limitation of prototype networks. Latent-space analysis via UMAP delineates semantically coherent sub-domains and maps zones of uncertainty where misclassifications concentrate, giving inspectors an explicit picture of where the model is-and is not-reliable. The framework is validated on an XCT patch dataset of approximately 12,000 patches extracted from four defect-rich SiC/SiC laboratory specimens. Taking a black-box ResNet-50 as a baseline (ROC-AUC = 0.991), the prototype extension achieves comparable performance (accuracy 0.957 vs. 0.959; ROC-AUC 0.994 vs. 0.993) while trading a slight reduction in sensitivity for higher precision and specificity. Each decision is backed by representative evidence patches, and the model explicitly flags its uncertainty regions. Beyond defect mapping, the framework establishes a reusable methodology for embedding domain-expert knowledge into prototype networks, applicable to other XCT inspection scenarios requiring traceable, auditable decisions.

翻译：航空航天SiC/SiC复合材料的X射线计算机断层扫描（XCT）无损检测依赖专家视觉评估，现行工作流程对合格/不合格决策的可追溯性有限。深度卷积网络虽能自动化缺陷检测，但其黑箱特性与工业检测实践要求的透明度相冲突。为弥合这一差距，我们提出p-ResNet-50——一种扩展原型层的卷积框架，该框架将高检测准确度与基于案例的解释相结合。六个学习到的原型与专家定义的语义类别（健康基体、基体-空气界面、孔隙、线状缺陷及混合形态）显式对齐，使得每次分类均可追溯至具有物理意义的参考依据。两项新颖的正则化项——基于锚点和基于中心点——将原型锚定至专家选取的补丁并防止原型坍塌，从而解决了原型网络已知的局限性。通过UMAP进行的潜在空间分析勾勒出语义一致的子域，并映射出误分类集中的不确定区域，为检测人员提供模型可靠性与不可靠性的可视化认知。该框架在约12,000个补丁的XCT补丁数据集上验证，这些补丁提取自四个富含缺陷的SiC/SiC实验室试样。以黑箱ResNet-50为基线（ROC-AUC=0.991），原型扩展框架在牺牲轻微灵敏度换取更高精确度和特异性的前提下，取得了可比的性能（准确率0.957 vs. 0.959；ROC-AUC 0.994 vs. 0.993）。每次决策均有代表性证据补丁支持，且模型显式标记其不确定区域。除缺陷映射外，该框架建立了将领域专家知识嵌入原型网络的可重用方法论，可推广至其他需可追溯、可审计决策的XCT检测场景。