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%.
|Number of pages||4|
|Journal||CIRP Annals - Manufacturing Technology|
|State||Published - Jan 2022|
Bibliographical noteFunding Information:
This work is supported by the National Science Foundation under grants CMMI-2024614 and CMMI-1830295 .
© 2022 CIRP
- Adaptive control
ASJC Scopus subject areas
- Mechanical Engineering
- Industrial and Manufacturing Engineering