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Combining Deep Learning and scatterControl for High-Throughput X-ray CT Based Non-Destructive Characterization of Large-Scale Casted Metallic Components

Journal Article · · Journal of Nondestructive Evaluation
 [1];  [2];  [1];  [1];  [3];  [2]
  1. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
  2. Carl ZEISS IMT GmbH, Oberkochen (Germany)
  3. ZEISS Industrial Quality Solutions, Maple Grove, MN (United States)
+ Show Author Affiliations
X-ray computed tomography (XCT) is essential for nondestructive evaluation and quality control of large-scale metal components. XCT imaging, however, faces significant challenges from metal artifacts, particularly those caused by Compton scattering, which degrade image quality and obscure critical details. Hardware-based solutions (e.g. scatterControl) offer advancements by intercepting scattered photons and reducing artifacts, but they can be time-consuming and require additional processing. Here, we propose modifying and leveraging a novel deep learning (DL) framework, Simurgh, to enhance and accelerate scatter correction in XCT. By combining scatterControl with DL-based artifact removal, we demonstrate significant reduction in scan time while producing high-quality reconstructions. Through extensive evaluation on industrial XCT data, we show that our methods reduce scan time by up to more than 10 x while preserving flaw detectability. Quantitative analysis across multiple segmentation techniques confirms that Simurgh-based reconstructions consistently outperform traditional Feldkamp-Davis-Kress, model-based iterative reconstruction, and commercial DL models in both pixel-level and task-specific evaluations, enabling scalable, high-throughput XCT workflows for characterization of large scale components in applications such as casting and metal additive manufacturing.
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:
3002416
Journal Information:
Journal of Nondestructive Evaluation, Journal Name: Journal of Nondestructive Evaluation Journal Issue: 4 Vol. 44; ISSN 0195-9298; ISSN 1573-4862
Publisher:
Springer NatureCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

Complex metal parts
Deep learning
Scattering
X-ray CT