Using Active Contour Models for Feature Extraction in Camera-Based Seam Tracking of Arc Welding

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In the recent decades much research has been performed in order to allow better control of arc welding processes, but the success has been limited, and the vast majority of the industrial structural welding work is therefore still being made manually. Closed-loop and nearly-closed-loop control of the processes requires the extraction of characteristic parameters of the welding groove close to the molten pool, i.e. in an environment dominated by the very intense light emission from the welding arc. The typical industrial solution today is a laser-scanner containing a camera as well as a laser source illuminating the groove by a light curtain and thus allowing details of the groove geometry to be extracted by triangulation. This solution is relatively expensive and must act several centimetres ahead of the molten pool. In addition laser-scanners often show problems when dealing with shiny surfaces. It is highly desirable to extract groove features closer to the arc and thus facilitate for a nearly-closed-loop control situation. On the other hand, for performing seam tracking and nearly-closed-loop control it is not necessary to obtain very detailed information about the molten pool area as long as some inportant features are obtained, e.g. the groove position and gap width. To obtain these features without external illumination, a new image analysis scheme based on active contour models was proposed and verified by experimental results.
Original languageEnglish
Title of host publicationProceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '09)
Publication date2009
ISBN (Print)978-1-4244-3804-4
Publication statusPublished - 2009
EventIEEE/RSJ International Conference on Intelligent Robots and Systems  IROS 2009 - St. Louis, United States
Duration: 11 Oct 200915 Oct 2009


ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems  IROS 2009
LandUnited States
BySt. Louis

    Research areas

  • Faculty of Science - Computer Science, Computer Vision, Welding, computer vision

ID: 16245944