Masters Theses

Title

Nuclear fuel pellet inspection using machine vision and artificial neural networks

Abstract

"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.

Advisor(s)

Keyvan, Shahla

Committee Member(s)

Moss, Randy Hays, 1953-
Kumar, A. S. (Arvind S.)

Department(s)

Mining and Nuclear Engineering

Degree Name

M.S. in Nuclear Engineering

Sponsor(s)

National Academy for Nuclear Training (U.S.)
University of Missouri--Rolla. School of Mines and Metallurgy
University of Missouri--Rolla. Department of Nuclear Engineering

Publisher

University of Missouri--Rolla

Publication Date

1995

Pagination

x, 116 leaves

Note about bibliography

Includes bibliographical references.

Rights

© 1995 Mark Le Roy Kelly, All rights reserved.

Document Type

Thesis - Citation

File Type

text

Language

English

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

Thesis Number

T 7074

Print OCLC #

34220944

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

This document is currently not available here.

Share

 
COinS