Abstract
Neural Networks are used to c lassify aluminum beverage containers as acceptable or non acceptable, depending upon their wall thicknesses. For each can, the thickness of the wall of the can at different points is measured using a non-destructive, ultra-sound technique. These measureI\}ents are then applied as inputs to the networks and the classification is provided as the output Three architectures, one unsupervised and two supervised, are tested. Their performances are analyzed and compared and the paradigm best suited to the problem is selected.
Recommended Citation
Balasubramaniam, B. and Clair, Daniel .., "Application of Neural Networks to the Non-Destructive Testing of Aluminum Cans" (1996). Computer Science Technical Reports. 191.
https://scholarsmine.mst.edu/comsci_techreports/191
Department(s)
Computer Science
Second Department
Mathematics and Statistics
Report Number
CSc-96-03
Document Type
Technical Report
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 1996 University of Missouri - Rolla, All rights reserved
Publication Date
1996-05-01
Comments
The first Author is a Graduate Student
This report is substantially the text of the M.S. thesis of the first author, completed May, 1996.
Acknowledgements:
I would like to thank Dr. D.C. St Clair for his continuous help and guidance on the project His insight and ideas were invaluable in the completion of the project. I would also like to express my thanks to my other committee members Dr. J. Prater and Prof. T. Herrick. Special thanks to Prof. Herrick for his interest and advice on the project. I would also like to thank Rick Gehring for help in collecting the data for the project.