About this Abstract |
| Meeting |
2025 AWS Professional Program
|
| Symposium
|
2025 AWS Professional Program
|
| Presentation Title |
Adaptive Approach for Live Control of Weldment Variations in Robotic Welding |
| Author(s) |
Ahmad Ashoori |
| On-Site Speaker (Planned) |
Ahmad Ashoori |
| Abstract Scope |
Root pass welding in Gas Metal Arc Welding (GMAW) is always challenging due to the nonlinear variations in part gaps and the presence of tacks. Robotic welding has recently gained a lot of attention to address the shortage of experienced welders. However, the adjustment of welding parameters (such as wire feed speed) and motion parameters (like travel speed) remains crucial for achieving a consistent, high-quality weld. Novarc’s AI-based welding solution uses an adaptive control approach to welding. It combines a vision-based system that replicates the perception of welders with real-time control to live-adjust welding and motion parameters based on the instantaneous pipe/part gap, learning about the tack and fusing it on the root pass. The controller monitors the state condition and communicates the proper process and motion update with the robot according to the real-time gap and tack state. The resulting closed-loop system enables higher quality and consistency of the weld. |
| Proceedings Inclusion? |
Undecided |