Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on the laser powder bed fusion process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environments in a nuclear reactor. Thermal tomography (TT) provides a capability for non-destructive evaluation of sub-surface defects in arbitrary size structures. We investigate TT of AM stainless steel 316L specimens with imprinted internal porosity defects using a relatively low-cost, small form factor infrared camera based on an uncooled micro-bolometer detector. Sparse coding-related K-means singular value decomposition machine learning, image processing algorithms are developed to improve the quality of TT images through removal of additive white Gaussian noise without blurring the images.

中文翻译：

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使用紧凑型红外相机和机器学习算法检测增材制造的不锈钢 316L 中的缺陷

增材制造 (AM) 是一种新兴的方法，用于经济高效地制造核反应堆部件。用于核能应用的金属结构的增材制造目前基于激光粉末床融合工艺，该工艺会引入内部材料缺陷，例如孔隙和各向异性。增材制造结构的完整性需要进行无损评估，因为材料缺陷可能会因暴露于核反应堆中的高温、辐射和腐蚀环境而导致过早失效。热断层扫描 (TT) 提供了对任意尺寸结构中的亚表面缺陷进行无损评估的能力。我们使用基于非制冷微测辐射热计检测器的成本相对较低、外形小巧的红外热像仪来研究具有印迹内部孔隙缺陷的 AM 不锈钢 316L 试样的 TT。