3D reconstruction of weld pool surface in pulsed GMAW by passive biprism stereo vision

Zhimin Liang, Hexi Chang, Qiyue Wang, Dianlong Wang, Yu Ming Zhang

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


Geometrical information of the 3D weld pool surface provides clues to determine the penetration state whose monitoring and feedback control are crucial for critical applications involving high temperatures and pressures. In this letter, a biprism stereo vision system is established to sense the weld pool surface under different penetration states during pulsed gas metal arc welding (GMAW-P) with a V groove joint. Only one optical filter is used to block out the arc and capture a clear image of the weld pool during the base current period in GMAW-P. While most of the weld pool surface is textureless, there are a few features such as slag that are detectable. Hence, during the stereo matching sparse feature corners are detected and credibly matched in pairs, followed by a disparity region growing step to produce a semi-dense and unambiguous disparity map. Polynomial interpolation is, then, applied to obtain subpixel disparity for every matched pixel. Finally, the point cloud and triangle mesh representation of the weld pool surface with different penetration states, partial, full, and over penetration, are presented. The experimental results verified the effectiveness of the proposed biprism stereo vision method in monitoring the weld penetration state.

Original languageEnglish
Article number8744587
Pages (from-to)3091-3097
Number of pages7
JournalIEEE Robotics and Automation Letters
Issue number3
StatePublished - Jul 2019

Bibliographical note

Funding Information:
Manuscript received February 14, 2019; accepted June 5, 2019. Date of publication June 24, 2019; date of current version July 3, 2019. This letter was recommended for publication by Associate Editor V. Krueger and Editor J. Li upon evaluation of the reviewers’ comments. This work was supported by the National Natural Science Foundation of China under Grant 51005069. (Corresponding author: Zhimin Liang.) Z. Liang, H. Chang, and D. Wang are with the School of Materials Science and Technology, Hebei University of Science and Technology, Shijiazhuang 050018, China (e-mail: lianghebust@163.com; changhexi914@126.com; uc3875@ 163.com).

Publisher Copyright:
© 2019 IEEE.


  • Computer vision for manufacturing
  • stereo vision
  • weld penetration state
  • weld pool monitoring

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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