Data-driven process characterization and adaptive control in robotic arc welding

Peng Wang, Joseph Kershaw, Matthew Russell, Jianjing Zhang, Yuming Zhang, Robert X. Gao

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Robotic arc welding (RAW) has been an essential process in various assembly systems, such as automotive manufacturing. However, its implementations lack adaptivity to compensate for process variations. This paper presents a data-driven process characterization and online adaptive control framework for RAW to automatically and efficiently achieve desired weld pool condition, given any initial conditions. Based on optical imaging, pool width is characterized through a pixel-level image segmentation network and then used for determining the parameter adjustment for robotic execution through a gradient-based controller. Experiments demonstrate quick process convergence within 7 adjustment periods and an error band within 10.9%.

Original languageEnglish
Pages (from-to)45-48
Number of pages4
JournalCIRP Annals - Manufacturing Technology
Volume71
Issue number1
DOIs
StatePublished - Jan 2022

Bibliographical note

Funding Information:
This work is supported by the National Science Foundation under grants CMMI-2024614 and CMMI-1830295 .

Publisher Copyright:
© 2022 CIRP

Keywords

  • Adaptive control
  • Robot
  • Welding

ASJC Scopus subject areas

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

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