A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Embedded processing methods for on-line visual analysis of laser welding
Tekijät: Olli Lahdenoja, Tero Säntti, Jonne K. Poikonen, Mika Laiho, Ari Paasio, Joonas Pekkarinen, Antti Salminen
Kustantaja: SPRINGER HEIDELBERG
Julkaisuvuosi: 2019
Journal: Journal of Real-Time Image Processing
Tietokannassa oleva lehden nimi: JOURNAL OF REAL-TIME IMAGE PROCESSING
Lehden akronyymi: J REAL-TIME IMAGE PR
Vuosikerta: 16
Numero: 4
Aloitussivu: 1099
Lopetussivu: 1116
Sivujen määrä: 18
ISSN: 1861-8200
DOI: https://doi.org/10.1007/s11554-016-0605-z
Tiivistelmä
Online monitoring and closed-loop control of laser welding offer great possibilities for achieving better weld quality. Earlier work on visual laser welding monitoring has mainly focused on aluminum and fairly thin steel used, for example, in car production. We extend this work by focusing on the automated analysis of the phenomena present in the laser welding of thick steel, where all of the phenomena related to the weld quality are still not well understood or controlled. This paper presents the implementation, test results and analysis for weld monitoring methods implemented on a compact smart camera system. The applied embedded sensor-processor platform allows for high-speed implementation of image capture and dynamic range compression, real-time seam tracking and spatter feature extraction. The paper describes experimental results from implemented real-time algorithms for seam tracking and spatter extraction and additional off-line analysis of methods for spatter tracking and seam widening detection, which are also feasible for future online hardware implementation. The results suggest that it is possible to integrate a compact laser welding analysis system, which achieves analysis rates that are sufficient for real-time process control.
Online monitoring and closed-loop control of laser welding offer great possibilities for achieving better weld quality. Earlier work on visual laser welding monitoring has mainly focused on aluminum and fairly thin steel used, for example, in car production. We extend this work by focusing on the automated analysis of the phenomena present in the laser welding of thick steel, where all of the phenomena related to the weld quality are still not well understood or controlled. This paper presents the implementation, test results and analysis for weld monitoring methods implemented on a compact smart camera system. The applied embedded sensor-processor platform allows for high-speed implementation of image capture and dynamic range compression, real-time seam tracking and spatter feature extraction. The paper describes experimental results from implemented real-time algorithms for seam tracking and spatter extraction and additional off-line analysis of methods for spatter tracking and seam widening detection, which are also feasible for future online hardware implementation. The results suggest that it is possible to integrate a compact laser welding analysis system, which achieves analysis rates that are sufficient for real-time process control.
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