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New CNN based algorithms for the full penetration hole extraction in laser welding processes

 
: Nicolosi, L.; Tetzlaff, R.; Abt, F.; Höfler, H.; Blug, A.; Carl, D.

:
Postprint urn:nbn:de:0011-n-1220219 (912 KByte PDF)
MD5 Fingerprint: 5cedb8610642862651de1e9a0d80088c
© 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Created on: 15.7.2010


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Circuits and Systems Society; National Cheng Kung University -NCKU-, Tainan:
IEEE International Symposium on Circuits and Systems, ISCAS 2009. Vol.5 : Taipei, Taiwan, 24 - 27 May 2009
Piscataway, NJ: IEEE, 2009
ISBN: 978-1-4244-3827-3
pp.2713-2716
International Symposium on Circuits and Systems (ISCAS) <2009, Taipei>
English
Conference Paper, Electronic Publication
Fraunhofer IPM ()
cellular neural network (CNN); laser welding; production engineering; manufacturing process

Abstract
In this paper new CNN based visual algorithms for the control of welding processes are proposed. The high dynamics of laser welding in several manufacturing processes ranging from automobile production to precision mechanics requires the introduction of new fast real time controls. In the last few years, analogic circuits like cellular neural networks (CNN) have obtained a primary place in the development of efficient electronic devices because of their real-time signal processing properties. Furthermore, several pixel parallel CNN based architectures are now included within devices like the family of EyeRis systems [1]. In particular, the algorithms proposed in the following have been implemented on the EyeRis system v1.2 with the aim to be run at frame rates up to 20 kHz.

: http://publica.fraunhofer.de/documents/N-122021.html