Effects of different joining geometries on cracking susceptibility and process efficiency using multi-alloy aluminum
Bibliographic record
Abstract
In the recent years, laser technology has been steadily growing in the field of car body fabrication. Typical laser welding applications are the joining of doors, door steps, floor groups, roof joints and hood parts. Since the introduction of the remote laser welding technology, flexible weld shapes are possible. This enables new space- and material-saving lightweight design. With this remote laser welding technology three different flange-reducing weld types were investigated analyzing their effect on cracking susceptibility and process efficiency: An overlap weld, a fillet weld, and a frontal edge weld. The results of these on-the-edge welds were compared with the state of the art welds positioned 10 mm away from the sheet edge. For the experiments a standard AA6xxx series alloy and a special multi-alloy which is known to be less crack sensitive were used. It is shown that combining frontal edge welding with multi-alloy aluminum enables high efficient and low crack sensitive welding processes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".