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An intelligent weld control strategy based on reinforcement learning approach

  • Zeshi Jin
  • Haichao LiEmail author
  • Hongming Gao
ORIGINAL ARTICLE
  • 171 Downloads

Abstract

Welding process control is an important part to realize intelligent welding. The actual welding process is a complex and nonlinear system influenced by multiple factors, such as welding current, arc voltage, welding speed, and so on. In addition, welding process is always interfered by working conditions, which make the reliability of general control model reduce greatly. So an intelligent weld control strategy that based on actor-critic reinforcement learning (ACRL) approach is selected to control the width of weld pool. And the gas tungsten arc welding (GTAW) and gas metal arc welding (GMAW) models are used to conduct simulation experiments of welding process control to verify the feasibility of the controller preliminarily. Finally, the opened-loop control experiment and the closed-loop control experiment are done, and the results are compared to verify the reliability of the controller.

Keywords

Actor-critic reinforcement learning GMAW Pool width Visual sensor technology Image processing Linear regression modeling 

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Notes

Funding information

This work was supported by National Natural Science Foundation of China, No. 51475102.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Advanced Welding and JoiningHarbin Institute of TechnologyHarbinPeople’s Republic of China

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