Welding Distortion Can be Mitigated if Welding Current and Traveling Speed Vary Optimized Along a Weld Path
Bibliographic record
Abstract
Typically, the distortion from welding is mitigated with the use of fixtures, clamps, tack welds and so on. Also the welding current and traveling speed are normally set constant during welding along a weld-path. The authors have developed and implemented an advanced control method that adaptively changes welding current and traveling speed depending on the state of deformation, in order to mitigate the final distortion without the use of additional hardware such as fixtures, clamps, and/or tack welds. It predicts the distortion before actual happening and adjusts parameters to counteract the deformation during welding. The present work implements this advanced method by applying an optimized, varying welding current and traveling speed on an edge-welded bar of Aluminum 5052-H32. A comparison is made between the final welding distortion with the new method, versus the regular method at constant welding current and traveling speed. A virtual predictive model was established to simulate and control the adaptive change of welding current and traveling speed, the optimized profile of the process parameters were performed by a robot, and the transient distortion was measured by state-of-the-art 3D photogrammetry cameras in real–time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".