Internal porosity remains a critical defect mode in additively manufactured components, compromising structural performance and limiting industrial adoption. Automated defect detection methods exist but lack interpretability, preventing engineers from understanding the physical basis of criticality predictions. This study presents an explainable computer vision framework for pore detection and criticality assessment in three-dimensional tomographic volumes. Sequential grayscale slices were reconstructed into volumetric datasets, and intensity-based thresholding with connected component analysis identified 500 individual pores. Each pore was characterized using geometric descriptors including size, aspect ratio, extent, and spatial position relative to the specimen boundary. A pore interaction network was constructed using percentile-based Euclidean distance criteria, yielding 24,950 inter-pore connections. Machine learning models predicted pore criticality scores from extracted features, and SHAP analysis quantified individual feature contributions. Results demonstrate that normalized surface distance dominates model predictions, contributing more than an order of magnitude greater importance than all other descriptors. Pore size provides minimal influence, while geometric parameters show negligible impact. The strong inverse relationship between surface proximity and criticality reveals boundary-driven failure mechanisms. This interpretable framework enables transparent defect assessment and provides actionable insights for process optimization and quality control in additive manufacturing.

翻译：内部孔隙仍然是增材制造构件中的关键缺陷模式，会损害结构性能并限制工业应用。现有的自动缺陷检测方法缺乏可解释性，使工程师无法理解临界性预测的物理基础。本研究提出了一种用于三维断层扫描体积中孔隙检测与临界性评估的可解释计算机视觉框架。通过将连续灰度切片重建为体积数据集，采用基于强度的阈值分割与连通分量分析识别出500个独立孔隙。每个孔隙均使用几何描述符进行表征，包括尺寸、纵横比、延展度以及相对于试样边界的空间位置。利用基于百分位数的欧氏距离准则构建孔隙相互作用网络，共生成24,950个孔隙间连接。机器学习模型根据提取的特征预测孔隙临界性评分，并通过SHAP分析量化各特征贡献度。结果表明：归一化表面距离主导模型预测，其重要性比其他所有描述符高出超过一个数量级。孔隙尺寸影响甚微，而几何参数的影响可忽略不计。表面邻近度与临界性之间的强负相关关系揭示了边界驱动的失效机制。该可解释框架实现了透明的缺陷评估，为增材制造的工艺优化与质量控制提供了可操作的见解。