Deep-learning based supervisory monitoring of robotized DE-GMAW process through learning from human welders

Rui Yu, Yue Cao, Jennifer Martin, Otto Chiang, Yu Ming Zhang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Double-electrode gas metal arc welding (DE-GMAW) modifies GMAW by adding a second electrode to bypass a portion of the current flowing from the wire. This reduces the current to, and the heat input on, the workpiece. Successful bypassing depends on the relative position of the bypass electrode to the continuously varying wire tip. To ensure proper operation, we propose robotizing the system using a follower robot to carry and adaptively adjust the bypass electrode. The primary information for monitoring this process is the arc image, which directly shows desired and undesired modes. However, developing a robust algorithm for processing the complex arc image is time-consuming and challenging. Employing a deep learning approach requires labeling numerous arc images for the corresponding DE-GMAW modes, which is not practically feasible. To introduce alternative labels, we analyze arc phenomena in various DE-GMAW modes and correlate them with distinct arc systems having varying voltages. These voltages serve as automatically derived labels to train the deep-learning network. The results demonstrated reliable process monitoring.

Original languageEnglish
Pages (from-to)781-791
Number of pages11
JournalWelding in the World, Le Soudage Dans Le Monde
Volume68
Issue number4
DOIs
StatePublished - Apr 2024

Bibliographical note

Publisher Copyright:
© International Institute of Welding 2023.

Keywords

  • Deep learning
  • Gas metal arc welding
  • Human welder
  • Robot

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering
  • Metals and Alloys

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