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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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July 2025
Latest News
DOE issues new NEPA rule and procedures—and accelerates DOME reactor testing
Meeting a deadline set in President Trump’s May 23 executive order “Reforming Nuclear Reactor Testing at the Department of Energy,” the DOE on June 30 updated information on its National Environmental Policy Act (NEPA) rulemaking and implementation procedures and published on its website an interim final rule that rescinds existing regulations alongside new implementing procedures.
K.-J. Boehm, Y. Ayzman, R. Blake, A. Garcia, K. Sequoia, S. Sundram, W. Sweet
Fusion Science and Technology | Volume 76 | Number 6 | August 2020 | Pages 749-757
Technical Paper | doi.org/10.1080/15361055.2020.1777673
Articles are hosted by Taylor and Francis Online.
Small shells, approximately 2 mm in diameter, made from Poly(α-methylstyrene) (PAMS) are used as mandrels in the production of glow discharge polymer capsules located at the center of inertial confinement fusion experiments. The visual inspection process of microscope images of these shell mandrels, including detection of micron-sized defects on the shell surface as well as the handling and sorting, is a very labor-intensive, repetitive, and highly subjective process that stands to benefit greatly from automation.
As part of an effort to decrease the number of labor hours spent in capsule handling, inspection, and metrology, the development of robotic systems was presented in a paper by Carlson et al., “Automation in Target Fabrication” [Fusion Sci. Technol., Vol. 70, p. 274 (2016)]. The current work expands the automated image acquisition systems developed previously and adds the use of convolutional neural networks to select capsules best suited for use in the downstream production process. Through the use of these machine learning algorithms, the selection process becomes robust, repeatable, and operator independent. As an added benefit the system developed as part of this work is able to provide defect statistics on entire shell batches and feed this information upstream to the production team.