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

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review

Contributors

  • Leonardo Nicolosi - , TUD Dresden University of Technology (Author)
  • Ronald Tetzlaff - , Chair of Fundamentals of Electrical Engineering (Author)
  • Felix Abt - , Forschungsgesellschaft für Strahlwerkzeuge Mbh (FGSW) (Author)
  • Andreas Blug - , Fraunhofer Institute for Physical Measurement Techniques (Author)
  • Daniel Carl - , Fraunhofer Institute for Physical Measurement Techniques (Author)
  • Heinrich Höfler - , Fraunhofer Institute for Physical Measurement Techniques (Author)

Abstract

In this paper the results obtained by the use of new CNN based visual algorithms for the control of welding processes 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 are not sufficient to control high speed laser welding processes due to the fast fluctuation of the full penetration hole. 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. In particular, new algorithms for the full penetration hole detection with frame rates 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.

Details

Original languageEnglish
Title of host publicationProceedings of 2009 International Joint Conference on Neural Networks
Pages2256-2263
Number of pages8
Publication statusPublished - 2009
Peer-reviewedYes

External IDs

Scopus 70449412539
ORCID /0000-0001-7436-0103/work/142240299

Keywords

Keywords

  • Cellular neural networks, closed loop systems, feature extraction, feedback, system application and experience., laser welding