Abstract
High-resolution X-ray computed tomography (XCT) is an important technique for the inspection of additively manufactured (AM) parts. While XCT is typically used off-line to inspect a subset of manufactured parts, significantly accelerating measurement speed while retaining accuracy would enable use of XCT for in-line inspection to rapidly identify defects in each part as it is manufactured. Here, we propose a deep learning (DL) based approach that uses computer aided design (CAD) models of the AM parts and physics-based information to rapidly produce high-quality reconstructions from sparse XCT measurements without high quality ground truth data. Our approach uses a generative adversarial neural network (GAN) to produced realistic training data from the CAD-based simulations and a deep neural network that is trained using data from the first stage to produce accurate 3D reconstructions. Using experimental XCT data of metal parts, we demonstrate enhanced defect detection capabilities while dramatically reducing the scan time.
Original language | English |
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Title of host publication | 2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings |
Publisher | IEEE Computer Society |
Pages | 3863-3867 |
Number of pages | 5 |
ISBN (Electronic) | 9781665496209 |
DOIs | |
State | Published - 2022 |
Event | 29th IEEE International Conference on Image Processing, ICIP 2022 - Bordeaux, France Duration: Oct 16 2022 → Oct 19 2022 |
Publication series
Name | Proceedings - International Conference on Image Processing, ICIP |
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ISSN (Print) | 1522-4880 |
Conference
Conference | 29th IEEE International Conference on Image Processing, ICIP 2022 |
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Country/Territory | France |
City | Bordeaux |
Period | 10/16/22 → 10/19/22 |
Funding
Corresponding author’s email address: [email protected]. Research sponsored by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Advanced Manufacturing Office, under contract DE-AC05-00OR22725 with UT-Battelle, LLC. The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Research sponsored by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Advanced Manufacturing Office, under contract DEAC05-00OR22725 with UT-Battelle, LLC. The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
Keywords
- Computer Aided Design (CAD)
- Deep Learning
- Domain Adaptation
- GANs
- Metal Additive Manufacturing (AM)
- XCT Reconstruction