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

Villarraga-Gómez, Herminso; Brackman, Paul; Ziabari, Amirkoushyar; Rahman, Obaidullah; Snow, Zackary; Shahani, Ravi; Bugelnig, Katrin; Andreyev, Andriy; Trenikhina, Yulia; Johnson, Nathan; Bale, Hrishikesh; Schulz, Julian; Santos, Edson Costa

doi:10.1007/s10921-025-01231-8

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

Journal Article · Mon Aug 18 00:00:00 EDT 2025 · Journal of Nondestructive Evaluation

DOI:https://doi.org/10.1007/s10921-025-01231-8· OSTI ID:2584476

Villarraga-Gómez, Herminso ^[1]; Brackman, Paul ^[1]; ^[2]; ^[2]; ^[2]; Shahani, Ravi ^[3]; Bugelnig, Katrin ^[4]; Andreyev, Andriy ^[5]; Trenikhina, Yulia ^[5]; Johnson, Nathan ^[5]; Bale, Hrishikesh ^[5]; Schulz, Julian ^[6]; Santos, Edson Costa ^[6]

Carl Zeiss Industrial Quality Solutions, LLC, Wixom, MI (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Constellium Technology Center, Voreppe (France)
German Aerospace Center (DLR), Cologne (Germany)
Carl Zeiss X-ray Microscopy, Inc, Dublin, CA (United States)
Carl Zeiss Industrielle Messtechnik GmbH, Oberkochen (Germany)

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 generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Materials & Manufacturing Technologies Office (AMMTO)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2584476

Journal Information:: Journal of Nondestructive Evaluation, Journal Name: Journal of Nondestructive Evaluation Journal Issue: 3 Vol. 44; ISSN 0195-9298; ISSN 1573-4862

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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