Bibliographic record
Abstract
The humping effect in laser beam welding has long been recognized as an adverse effect that limits the achievable welding speed in high power laser beam welding applications. By nature, this effect results in a rough weld bead in laser beam welding and affects the weld integrity. In the past, efforts have been made to suppress the humping effect in laser welding. However, it has been found that the humping effect can have a practical use during the preprocess stage for generating “dimples” for laser beam lap welding of zinc coated sheet metals. Remote laser welding takes advantage of less mechanical movement and better accessibility of the beam to the workpiece, thus fast processing speed can be achieved. Furthermore, currently remote laser welding is mostly suitable for lap joints. However, 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 vapor which is generated in the interface during welding. Laser dimpling appears to be one of the most efficient and practical methods to produce localized gap for venting zinc vapor during welding. Laser dimpling can be achieved using a moving laser pulse at a typical welding speed. Due to its high speed phenomenon, the humping effect is an ideal high-speed process to generate dimples. It has been demonstrated that the humping effect is a cost effective method to be used in a preprocess stage for remote laser lap welding of zinc coated sheet metals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".