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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
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Erstellt am: 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
International Symposium on Circuits and Systems (ISCAS) <2009, Taipei>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IPM ()
cellular neural network (CNN); laser welding; production engineering; manufacturing process

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.