DocumentCode :
2534610
Title :
A cellular neural networks approach for non-destructive control of mechanical parts
Author :
Bertucco, L. ; Fargione, G. ; Nunnari, G. ; Risitano, A.
Author_Institution :
Dept. of Electr. Electron. & Syst., Catania Univ., Italy
fYear :
2000
fDate :
2000
Firstpage :
159
Lastpage :
164
Abstract :
An approach is proposed using cellular neural networks applied image processing, for the detection and characterisation of superficial faults in mechanical parts. There are above all two advantages deriving from an application of the proposed methodologies: the automization of a procedure, that of non-destructive tests (NDT), which is today carried out manually, and the possibility to reduce to a negligible amount the time spent on checking operations at present estimated to be in the order of a number of hours for each separate mechanical part
Keywords :
cellular neural nets; image processing; nondestructive testing; mechanical parts; nondestructive control; superficial faults; Automatic control; Cellular neural networks; Electrical equipment industry; Electronic mail; Electronics industry; Image processing; Industrial electronics; Mechanical engineering; Nondestructive testing; Stress;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cellular Neural Networks and Their Applications, 2000. (CNNA 2000). Proceedings of the 2000 6th IEEE International Workshop on
Conference_Location :
Catania
Print_ISBN :
0-7803-6344-2
Type :
conf
DOI :
10.1109/CNNA.2000.876838
Filename :
876838
Link To Document :
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