2023
DOI: 10.1108/ir-06-2022-0153
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Robotic friction stir welding – seam-tracking control, force control and process supervision

Abstract: Purpose This study aims to enable robotic friction stir welding (FSW) in practice. The use of robots has hitherto been limited, because of the large contact forces necessary for FSW. These forces are detrimental for the position accuracy of the robot. In this context, it is not sufficient to rely on the robot’s internal sensors for positioning. This paper describes and evaluates a new method for overcoming this issue. Design/methodology/approach A closed-loop robot control system for seam-tracking control an… Show more

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Cited by 3 publications
(1 citation statement)
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References 19 publications
(27 reference statements)
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“…Guan et al [61] used machine learning, and Rabe et al [62] used a deep learning approach to determine weld quality based on force characteristics during welding. Karlsoon et al [63] used an industrial robot to develop a closed-loop control system for seam-tracking and force control during welding. The welds formed were defect-free, proving robots can be used effectively to conduct the welding process.…”
Section: Fsw Modellingmentioning
confidence: 99%
“…Guan et al [61] used machine learning, and Rabe et al [62] used a deep learning approach to determine weld quality based on force characteristics during welding. Karlsoon et al [63] used an industrial robot to develop a closed-loop control system for seam-tracking and force control during welding. The welds formed were defect-free, proving robots can be used effectively to conduct the welding process.…”
Section: Fsw Modellingmentioning
confidence: 99%