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

Author

Listed:
  • Zeqi Hu

    (Wuhan University of Technology
    Hubei Key Laboratory of Advanced Technology for Automotive Components
    Hubei Collaborative Innovation Center for Automotive Components Technology)

  • Xunpeng Qin

    (Wuhan University of Technology
    Hubei Key Laboratory of Advanced Technology for Automotive Components
    Hubei Collaborative Innovation Center for Automotive Components Technology)

  • Yifeng Li

    (Wuhan University of Technology
    Hubei Key Laboratory of Advanced Technology for Automotive Components
    Hubei Collaborative Innovation Center for Automotive Components Technology)

  • Jiuxin Yuan

    (Wuhan University of Technology
    Hubei Key Laboratory of Advanced Technology for Automotive Components
    Hubei Collaborative Innovation Center for Automotive Components Technology)

  • Qiang Wu

    (Wuhan University of Technology
    Hubei Key Laboratory of Advanced Technology for Automotive Components
    Hubei Collaborative Innovation Center for Automotive Components Technology)

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.

Suggested Citation

  • Zeqi Hu & Xunpeng Qin & Yifeng Li & Jiuxin Yuan & Qiang Wu, 2020. "Multi-bead overlapping model with varying cross-section profile for robotic GMAW-based additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1133-1147, June.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:5:d:10.1007_s10845-019-01501-z
    DOI: 10.1007/s10845-019-01501-z
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    References listed on IDEAS

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    1. Biranchi Panda & K. Shankhwar & Akhil Garg & M. M. Savalani, 2019. "Evaluation of genetic programming-based models for simulating bead dimensions in wire and arc additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 809-820, February.
    2. Yicha Zhang & Alain Bernard & Ramy Harik & K. P. Karunakaran, 2017. "Build orientation optimization for multi-part production in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1393-1407, August.
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    Cited by:

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    2. Chenglin Li & Baohai Wu & Zhao Zhang & Ying Zhang, 2023. "A novel process planning method of 3 + 2-axis additive manufacturing for aero-engine blade based on machine learning," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 2027-2042, April.
    3. Chunyang Xia & Zengxi Pan & Joseph Polden & Huijun Li & Yanling Xu & Shanben Chen, 2022. "Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1467-1482, June.

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