Abstract
Nuclear fuel must be of high quality before being placed into service in a reactor. Fuel vendors currently use manual inspection for quality control of fabricated nuclear fuel pellets. In order to reduce workers' exposure to radiation and increase the inspection accuracy and speed, the feasibility of automation of fuel pellet inspection using artificial neural networks (ANNs) is studied in this paper. Three kinds of neural network architectures are examined for evaluation of the ANN performance in proper classification of good versus bad pellets. Two supervised neural networks, back-propagation and fuzzy ARTMAP, and one unsupervised neural network called ART2-A are applied. The results indicate that a supervised ANN with adequate training can achieve a high success rate in classification of fuel pellets. © 1999 Elsevier Science B.V. All rights reserved.
Recommended Citation
S. Keyvan et al., "Nuclear Fuel Pellet Inspection using Artificial Neural Networks," Journal of Nuclear Materials, vol. 264, no. 1 thru 2, pp. 141 - 154, Elsevier, Jan 1999.
The definitive version is available at https://doi.org/10.1016/S0022-3115(98)00464-4
Department(s)
Nuclear Engineering and Radiation Science
International Standard Serial Number (ISSN)
0022-3115
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
01 Jan 1999
