A1 Refereed original research article in a scientific journal
Embedded processing methods for on-line visual analysis of laser welding
Authors: Olli Lahdenoja, Tero Säntti, Jonne K. Poikonen, Mika Laiho, Ari Paasio, Joonas Pekkarinen, Antti Salminen
Publisher: SPRINGER HEIDELBERG
Publication year: 2019
Journal: Journal of Real-Time Image Processing
Journal name in source: JOURNAL OF REAL-TIME IMAGE PROCESSING
Journal acronym: J REAL-TIME IMAGE PR
Volume: 16
Issue: 4
First page : 1099
Last page: 1116
Number of pages: 18
ISSN: 1861-8200
DOI: https://doi.org/10.1007/s11554-016-0605-z
Abstract
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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