Nuclear fuel pellet inspection using machine vision and artificial neural networks
"Nuclear fuel must be of high quality before being placed into service in a reactor. Nuclear fuel vendors currently use manual inspection to verify the quality of the nuclear fuel before the fuel pellets are inserted into the zirconium fuel rods and bundled into assemblies. This work examines the feasibility of using machine vision and artificial neural networks to perform the same task, with motivation being to improve accuracy, speed, costs, reduce employee radiation doses, and to provide defect statistics to the fuel manufacturer. Sample nuclear fuel pellets were photographed, scanned, and an appropriate feature extraction technique was developed and applied to the scanned images. The extracted features were then used as inputs to two neural network paradigms, ART2-A and backpropagation. The results of testing were then compared for the two networks. Both the ART2-A and backpropagation results are promising. Results indicate that a machine vision inspection system may be possible using a system of ART2-A networks or a single backpropagation neural network"--Abstract, leaf iii.
Moss, Randy Hays, 1953-
Kumar, A. S. (Arvind S.)
Mining and Nuclear Engineering
M.S. in Nuclear Engineering
National Academy for Nuclear Training (U.S.)
University of Missouri--Rolla. School of Mines and Metallurgy
University of Missouri--Rolla. Department of Nuclear Engineering
University of Missouri--Rolla
x, 116 leaves
© 1995 Mark Le Roy Kelly, All rights reserved.
Thesis - Citation
Library of Congress Subject Headings
Nuclear fuels -- Quality control
Nuclear fuels -- Inspection
Quality control -- Automation
Computer vision -- Industrial applications
Neural networks (Computer science) -- Industrial applications
Back propagation (Artificial intelligence) -- Industrial applications
Nuclear fuels -- Defects
Print OCLC #
Link to Catalog Record
Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.http://laurel.lso.missouri.edu:80/record=b2766154~S5
Kelly, Mark Le Roy, "Nuclear fuel pellet inspection using machine vision and artificial neural networks" (1995). Masters Theses. 5992.
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