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Feature extraction in laser welding processes

 
: Geese, M.; Tetzlaff, R.; Carl, D.; Blug, A.; Höfler, H.; Abt, F.

:
Fulltext urn:nbn:de:0011-n-832088 (4.0 MByte PDF)
MD5 Fingerprint: 3bd5f98787560786fdee1b982cfd00a8
© 2008 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: 12.11.2008


López Vilariño, D. ; Institute of Electrical and Electronics Engineers -IEEE-:
11th International Workshop on Cellular Neural Networks and their Applications, CNNA 2008 : Santiago de Composteia i.e. Compostela, Spain, 14 - 16 July 2008
New York, NY: IEEE, 2008
ISBN: 978-1-4244-2089-6
ISBN: 978-1-4244-2090-2
pp.196-201
International Workshop on Cellular Neural Networks and their Applications (CNNA) <11, 2008, Santiago de Compostela>
English
Conference Paper, Electronic Publication
Fraunhofer IPM ()

Abstract
There is a rapidly growing demand for laser welding in a wide variety of manufacturing processes ranging from automobile production to precision mechanics. Up to now, the high dynamics of the process has made it impossible to construct a camera based real time quality and process control. Since new pixel parallel architectures are existing, which are now available in systems such as the ACE16k, Q- Eye, and SCAMP-3 (P. Dudek et al., 2006), one has become able to implement a real time laser welding processing. In this paper we will propose a feature extraction algorithm, running at a frame rate of 10 kHz, for a laser welding process. The performance of the algorithm has been studied in detail. In particular, it has been implemented on an Eye-RIS v.1.1 system and has been applied to laser welding processes.

: http://publica.fraunhofer.de/documents/N-83208.html