Remote laser welding of zinc-coated sheet metal component in a lap configuration utilizing humping effect
Bibliographic record
Abstract
With the advancement of high power fiber-delivery lasers, remote laser welding becomes a reality and more and more systems have been introduced into production lines. Remote laser welding takes the advantages of less mechanical movement and better accessibility of the beam to the workpiece, thus fast processing speed can be achieved. In most cases, remote laser welding involves lap welding. Laser beam lap welding of zinc coated steel components is not a straightforward process and it requires a special procedure to provide proper venting for the zinc vapour which is generated during welding in the interface. Although there are many approaches to address this issue, many of the approaches are either impractical or too costly to apply to remote laser beam welding in production. Humping effect is an adverse effect that limits the achievable welding speed in laser beam welding. However, it has been demonstrated in the experiment that the height of the protuberance in the humping effect can be controlled via proper process parameters and an optimal height in the range of 0.1 – 0.2 mm can be achieved to meet vapour venting requirement. In reality, the optimum gap size is depending on material thickness and coating type. As a result, the high-speed humping effect can be advantageously used as a pre-process to provide proper gap for laser beam lap welding of zinc coated sheet metals. Furthermore, this pre-process can be naturally implemented in the remote laser welding process flow. The whole process is very flexible and can be achieved at very high speed.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".