Nuclear fuel pellet inspection using artificial neural networks

Nuclear fuel must be of high quality before being placed into service in a reactor. Fuel vendors currently use manual inspection for quality control o f fabricated nuclear fuel pellets. In order to reduce workers' exposure to radiation and increase the inspection accuracy and speed, the feasibility o f automation of fuel pellet inspection using artificial neural networks (AN Ns) is studied in this paper. Three kinds of neural network architectures a re examined for evaluation of the ANN performance in proper classification of good versus bad pellets. Two supervised neural networks, backpropagation and fuzzy ARTMAP, and one unsupervised neural network called ART2-A are ap plied. The results indicate that a supervised ANN with adequate training ca n achieve a high success rate in classification of fuel pellets. (C) 1999 E lsevier Science B.V. All rights reserved.