Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/319241 
Erscheinungsjahr: 
2024
Quellenangabe: 
[Journal:] Journal of Intelligent Manufacturing [ISSN:] 1572-8145 [Volume:] 36 [Issue:] 3 [Publisher:] Springer US [Place:] New York, NY [Year:] 2024 [Pages:] 1711-1721
Verlag: 
Springer US, New York, NY
Zusammenfassung: 
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.
Schlagwörter: 
Wire arc additive manufacturing
Vision based monitoring
Machine learning
Nozzle-to-work distance monitoring
Contact tube wear off detection
Spatter detection
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Dokumentart: 
Article
Dokumentversion: 
Published Version

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