Automatic inspection for remotely manufactured fuel elements [electronic resource].

Published:: Washington, D.C. : United States. Dept. of Energy, 1995.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
Physical Description:: 9 pages : digital, PDF file
Additional Creators:: Argonne National Laboratory, United States. Department of Energy, and United States. Department of Energy. Office of Scientific and Technical Information

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Summary:

Two classification techniques, standard control charts and artificial neural networks, are studied as a means for automating the visual inspection of the welding of end plugs onto the top of remotely manufactured reprocessed nuclear fuel element jackets. Classificatory data are obtained through measurements performed on pre- and post-weld images captured with a remote camera and processed by an off-the-shelf vision system. The two classification methods are applied in the classification of 167 dummy stainless steel (HT9) fuel jackets yielding comparable results.

Report Numbers:

E 1.99:anl/ra/cp--82800
E 1.99: conf-950232--34
conf-950232--34
anl/ra/cp--82800

Subject(s):

Other Subject(s):

Note:

Published through SciTech Connect.
06/01/1995.
"anl/ra/cp--82800"
" conf-950232--34"
"DE95012289"
6. American Nuclear Society meeting on robotics and remote systems, Monterey, CA (United States), 5-10 Feb 1995.
Benedict, R.W.; Reifman, J.; Vitela, J.E.; Gibbs, K.S.

Funding Information:

W-31-109-ENG-38

View MARC record | catkey: 14111936