Mitigation of Distortion in an Edge-Welded Bar by Optimization Control of the Welding Current and Speed Profile During Welding
Bibliographic record
Abstract
Welding distortion is usually controlled by clamping techniques that can be tack welds, pre-bending, and tension loading. Side heating or fast cooling can also mitigate the distortion in some applications. In addition to the clamping techniques, process parameters affect the distortion so that if one can control the welding process parameters, an optimized profile of such parameters could alleviate the distortion. It is shown in this paper that the distortion can be mitigated by using an optimized profile of welding current and travelling speed. These profiles keep the power per unit length of welding constant. It is shown that an increasing welding current at the beginning and the end of the welding path on an edge welded bar of Aluminum could result in a bar that is closer to flat compared to the constant welding current. Developing an optimized weld process parameter profile requires a trustable computational model to implement a control problem using a predictive model for distortion in front of the weld pool in order to adjust the welding current and speed. Unlike using a constant welding current for the full path of weld, the path length is divided into several sub-paths. For each of weld sub-path the control problem learns from the previous sub-path and tries to find the new value for the welding current and speed that minimize the distortion using predictive Computational Weld Mechanics (CWM). Final deflections of the bar are also compared between a constant welding current and optimized profile of welding current.
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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 | 0.001 | 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".