Vision based process monitoring in wire arc additive manufacturing (WAAM)

Jan Franke (), Florian Heinrich () and Raven T. Reisch ()
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Jan Franke: University of Passau
Florian Heinrich: University of Passau
Raven T. Reisch: Technical University of Munich (TUM)

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 3, No 10, 1721 pages

Abstract: Abstract A stable welding process is crucial to obtain high quality parts in wire arc additive manufacturing. The complexity of the process makes it inherently unstable, which can cause various defects, resulting in poor geometric accuracy and material properties. This demands for in-process monitoring and control mechanisms to industrialize the technology. In this work, process monitoring algorithms based on welding camera image analysis are presented. A neural network for semantic segmentation of the welding wire is used to monitor the working distance as well as the horizontal position of the wire during welding and classic image processing techniques are applied to capture spatter formation. Using these algorithms, the process stability is evaluated in real time and the analysis results enable the direction independent closed-loop-control of the manufacturing process. This significantly improves geometric fidelity as well as mechanical properties of the fabricated part and allows the automated production of parts with complex deposition paths including weld bead crossings, curvatures and overhang structures.

Keywords: Wire arc additive manufacturing; Vision based monitoring; Machine learning; Nozzle-to-work distance monitoring; Contact tube wear off detection; Spatter detection (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-023-02287-x

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