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Detection of fillet weld joints using an adaptive line growing algorithm for robotic arc welding
Authors:Mitchell Dinham  Gu Fang
Affiliation:School of Computing, Engineering & Mathematics, University of Western Sydney, Australia
Abstract:One of the main challenges for robotic welding in low to medium volume manufacturing or repair work is the time taken to programme the robot path for a new job. It is often cheaper and more efficient to weld the parts manually. There are many papers published on the detection of butt welds, however there is no mature method for the identification of fillet welds which are more common. This paper presents a novel method that can autonomously identify fillet weld joints regardless of the base material, surface finish and surface imperfections such as scratches, mill scale and rust. The new method introduces an adaptive line growing algorithm for robust identification of weld joints regardless of the shape of the seam. The proposed method is validated through experiments using an industrial welding robot in a workshop environment. The results show that this method can detect realistic fillet weld joints for industrial arc welding applications.
Keywords:Robotic arc welding  Fillet weld  Welding seam detection
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