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Erschienen in: Journal of Intelligent Manufacturing 5/2020

24.10.2019

Multi-bead overlapping model with varying cross-section profile for robotic GMAW-based additive manufacturing

verfasst von: Zeqi Hu, Xunpeng Qin, Yifeng Li, Jiuxin Yuan, Qiang Wu

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 5/2020

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Abstract

In robotic GMAW-based additive manufacturing, the surface evenness of the deposited layer was significant to the dimensional accuracy and the stable fabrication process, and it was determined by the multi-bead overlapping distance. To obtain the optimal overlapping distance, a group of two-bead overlapping experiments was conducted with different overlapping ratio. The cross-section shape was observed and the variation of the bead profile caused by the damming up of the previous bead was investigated. The second bead profile could be fitted by a rotated varying parabola or circular arc function with the decreasing of the overlapping distance from the initial single bead width (w) to 0. A varying cross-section profile overlapping model was developed based on the actual forming characteristics of the overlapping experiment, through which the varying profile of two overlapping beads with arbitrary distance could be predicted. Then, the optimal overlapping distance was calculated under some principles to achieve a relatively flat top surface and stable overlapping process, and the multi-bead overlapping experiments were performed to validate the model. The results showed that the model could achieve an excellent approximation to the actual overlapping experiment, and the good surface evenness and stable overlapping process was obtained, which was significant to the research into the appearance optimization in GMAW-based additive manufacturing.

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Literatur
Zurück zum Zitat Las-Casas, M. S., de Ávila, T. L. D., Bracarense, A. Q., & Lima, E. J. (2018). Weld parameter prediction using artificial neural network: FN and geometric parameter prediction of austenitic stainless steel welds. Journal of the Brazilian Society of Mechanical Sciences and Engineering,40(1), 26. https://doi.org/10.1007/s40430-017-0928-0.CrossRef Las-Casas, M. S., de Ávila, T. L. D., Bracarense, A. Q., & Lima, E. J. (2018). Weld parameter prediction using artificial neural network: FN and geometric parameter prediction of austenitic stainless steel welds. Journal of the Brazilian Society of Mechanical Sciences and Engineering,40(1), 26. https://​doi.​org/​10.​1007/​s40430-017-0928-0.CrossRef
Zurück zum Zitat Li, Y., Han, Q., Zhang, G., & Horváth, I. (2018). A layers-overlapping strategy for robotic wire and arc additive manufacturing of multi-layer multi-bead components with homogeneous layers. The International Journal of Advanced Manufacturing Technology,96(9–12), 3331–3344. https://doi.org/10.1007/s00170-018-1786-3.CrossRef Li, Y., Han, Q., Zhang, G., & Horváth, I. (2018). A layers-overlapping strategy for robotic wire and arc additive manufacturing of multi-layer multi-bead components with homogeneous layers. The International Journal of Advanced Manufacturing Technology,96(9–12), 3331–3344. https://​doi.​org/​10.​1007/​s00170-018-1786-3.CrossRef
Metadaten
Titel
Multi-bead overlapping model with varying cross-section profile for robotic GMAW-based additive manufacturing
verfasst von
Zeqi Hu
Xunpeng Qin
Yifeng Li
Jiuxin Yuan
Qiang Wu
Publikationsdatum
24.10.2019
Verlag
Springer US
Erschienen in
Journal of Intelligent Manufacturing / Ausgabe 5/2020
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI
https://doi.org/10.1007/s10845-019-01501-z

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