Robotic Adaptive Algorithm for Solving Fit-up Variations in Welding at Industrial Scale
Why this work is in the frame
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Bibliographic record
Abstract
Open root pass welding in Gas Metal Arc Welding (GMAW) is always challenging due to the nonlinear random variations in pipe gaps and the presence of tacks.Manual welding requires a lot of skill from senior welders to react and control many variables promptly.In the transition to robotic welding, tracking solutions based on laser or vision systems have emerged to address the tracking issue.However, adapting the welding parameters (e.g.wire feed speed) and motion parameters (e.g.travel speed) is still essential in getting a consistent, high-quality weld.This work presents an adaptive control approach to pipe welding.The method combines a visionbased system that replicates the perception of welders with real-time control to live-adjust welding and motion parameters based on the instantaneous pipe gap, learning about the tack and fusing it on the root pass -a critical challenge for robotic welding applications.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 weld throughout the pipe welding.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it