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

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

Postprint urn:nbn:de:0011-n-1220222 (2.5 MByte PDF)
MD5 Fingerprint: b87c1b86371d384d5c9bcd7cd55d7dda
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Erstellt am: 8.7.2010

International Neural Network Society; IEEE Computational Intelligence Society:
International Joint Conference on Neural Networks, IJCNN 2009. Vol.4 : Atlanta, Georgia, USA, 14 - 19 June 2009
Piscataway, NJ: IEEE, 2009
ISBN: 978-1-4244-3549-4
ISBN: 978-1-4244-3548-7
ISBN: 978-1-4244-3553-1
International Joint Conference on Neural Networks (IJCNN) <2009, Atlanta/Ga.>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IPM ()
cellular neural network (CNN); laser welding; production engineering; manufacturing process; production engineering

In this paper the results obtained by the use of new CNN based visual algorithms for the control of welding proces ses are described. The growing number of laser welding applications from automobile production to micro mechanics requires fast systems to create closed loop control for error prevention and correction. Nowadays the image processing frame rates of conventional architectures [1] are not sufficient to control high speed laser welding processes due to the fast fluctuation of the full penetration hole [3]. This paper focuses the attention on new strategies obtained by the use of the Eye-RIS system v1.2 which includes a pixel parallel Cellular Neural Network (CNN) based architecture called Q-Eye [2]. In particular, new algorithms for the full penetration hole detection with frame r ates up to 24 kHz will be presented. Finally, the results obtained performing real time control of welding processes by the use of these algorithms will be discussed.