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Advancing Additive Manufacturing Through Artificial Intelligence–Powered, High-Throughput, Nondestructive Characterization and Process Optimization

Technical Report ·
DOI:https://doi.org/10.2172/3000516· OSTI ID:3000516
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  1. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
  2. Carl Zeiss Industrial Metrology, LLC, Oberkochen (Germany)
+ Show Author Affiliations
This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and ZEISS Industrial Metrology has demonstrated the transformative potential of artificial intelligence (AI)-enabled x-ray computed tomography (XCT) to accelerate the qualification and certification of additively manufactured (AM) parts. At the core of this effort is Simurgh, an AI-powered XCT reconstruction framework jointly advanced by ORNL and ZEISS that integrates computer-aided design (CAD) models, physics-based simulations, and deep learning to overcome the long-standing challenges of metal artifact correction, long scan durations, and limited flaw detectability in dense and geometrically complex components. Simurgh enables high-throughput, high-quality 3D reconstruction from sparse and fast scans, which reduces XCT acquisition times by more than an order of magnitude and simultaneously improves defect detection limits by up to fourfold compared with industry-standard approaches. This capability reduces scan costs by more than 50%, lowers labor overhead, and makes XCT characterization economically viable for routine industrial use. By enabling reliable flaw detection in minutes rather than hours, Simurgh facilitates real-time feedback loops for process parameter optimization, which was highlighted in a recent npj Computational Materials (a Nature journal) issue. In the published study, more than 100 alloy coupons were characterized within a single day. This work represents a tenfold acceleration in the development of novel AM alloys and processes compared with conventional workflows. The ZEISS collaboration has also demonstrated the scalability of Simurgh to diverse application domains, including aerospace, nuclear, automotive, and biomedical components; in these applications, ensuring structural integrity is paramount. By drastically reducing barriers to XCT adoption, this partnership has laid the foundation for digital twins and data-driven certification pipelines and directly addressed bottlenecks in qualifying new materials and designs. Together, ORNL and ZEISS have shown that Simurgh advances the state of the art in nondestructive evaluation and aligns with the broader mission of enabling Industry 4.0 manufacturing ecosystems, in which intelligent, cost-effective, rapid quality assurance is integral to accelerating innovation and ensuring safety in critical applications.
Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Carl Zeiss Industrial Metrology, LLC, Oberkochen (Germany)
Sponsoring Organization:
USDOE Office of Science (SC)
DOE Contract Number:
AC05-00OR22725
OSTI ID:
3000516
Report Number(s):
ORNL/TM--2025/4207; NFE-19-07705
Country of Publication:
United States
Language:
English

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36 MATERIALS SCIENCE
97 MATHEMATICS AND COMPUTING
artificial intelligence
additive manufacturing
XCT