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Control of weld penetration depth using relative fluctuation coefficient as feedback

Author

Listed:
  • Shuangyang Zou

    (Tianjin University)

  • Zhijiang Wang

    (Tianjin University)

  • Shengsun Hu

    (Tianjin University)

  • Wandong Wang

    (Tianjin University)

  • Yue Cao

    (Tianjin University)

Abstract

The monitoring and control of weld penetration in pulsed gas metal arc welding (GMAW-P) is considerably challenging, especially in field applications. The metal transfer and pulse current in GMAW-P complicate the identification of weld penetration. In previous studies, the authors found that both the change in arc voltage during the peak current period and the average arc voltage during the peak current period can be used for condition monitoring of weld pool surface and thus for the estimation of GMAW-P penetration depth. In the present work, the relative fluctuation coefficient (CRF) of weld pool surface is proposed by combining these two signals to predict the weld penetration depth. Model predictive control using this coefficient as feedback is employed to control the penetration depth. The experimental results show that uniform weld penetration depth can be obtained by the adaptive control algorithm. The practice attempted in this work can be expected to be a candidate solution for GMAW-P penetration control, which is easy to implement in field applications.

Suggested Citation

  • Shuangyang Zou & Zhijiang Wang & Shengsun Hu & Wandong Wang & Yue Cao, 2020. "Control of weld penetration depth using relative fluctuation coefficient as feedback," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1203-1213, June.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:5:d:10.1007_s10845-019-01506-8
    DOI: 10.1007/s10845-019-01506-8
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    Cited by:

    1. Kaiser Asif & Lu Zhang & Sybil Derrible & J. Ernesto Indacochea & Didem Ozevin & Brian Ziebart, 2022. "Machine learning model to predict welding quality using air-coupled acoustic emission and weld inputs," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 881-895, March.

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