Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizi. Authors: Villarraga-Gómez, Herminso [Carl Zeiss Industrial Quality Solutions, LLC, Wixom, MI (United States)]; Brackman, Paul [Carl Zeiss Industrial Quality Solutions, LLC, Wixom, MI (United States)]; Ziabari, Amirkoushyar [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:000000034776457X); Rahman, Obaidullah [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000277810840); Snow, Zackary [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000159016785). DOE Contract: AC05-00OR22725. Subjects: Additive manufacturing; Computed tomography; Deep learning; Machining; Materials Characterization Technique; Materials Engineering; Metrology; Non-destructive evaluation; X-Ray Diffraction; X-ray Tomography; X-ray microscopy