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  4. New CNN based algorithms for the full penetration hole extraction in laser welding processes
 
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2009
Conference Paper
Title

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

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.
Author(s)
Nicolosi, L.
Tetzlaff, R.
Abt, F.
Höfler, H.  
Fraunhofer-Institut für Physikalische Messtechnik IPM  
Blug, A.  
Fraunhofer-Institut für Physikalische Messtechnik IPM  
Carl, D.  
Fraunhofer-Institut für Physikalische Messtechnik IPM  
Mainwork
IEEE International Symposium on Circuits and Systems, ISCAS 2009. Vol.5  
Conference
International Symposium on Circuits and Systems (ISCAS) 2009  
Open Access
File(s)
Download (912.62 KB)
Rights
Use according to copyright law
DOI
10.24406/publica-r-364292
10.1109/ISCAS.2009.5118362
Language
English
Fraunhofer-Institut für Physikalische Messtechnik IPM  
Keyword(s)
  • cellular neural network (CNN)

  • laser welding

  • production engineering

  • manufacturing process

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