Factors controlling keyhole-induced porosity in cold wire laser welded aluminum
Bibliographic record
Abstract
Production of lightweight laser-welded assemblies to meet the requirements for higher performance and reduction of carbon emission requires substantial effort in mitigating the effects of processing on welding defects. The factors controlling keyhole-induced porosity during cold wire laser welding of AA6061-T6 were studied. The laser beam intensity distribution parameters and the laser energy deposited per unit length of the weld determine the keyhole behavior, the shape and size of the surrounding molten material and, consequently, the percentage of porosity and average size of pores that resulted from keyhole collapse during welding. Changes in keyhole size and porosity are not noticeably significant with respect to the rate of feeding the cold wire into the molten weld pool. The vapor plume that forms above the molten pool due to the ejection of evaporated metal particles from the keyhole effectively attenuates the laser beam and reduces the depth of penetration. However, the erratic behavior of the plume results in an inconsistent porosity response. The use of a gas jet for deflecting the plume is beneficial for more consistent porosity response. Results from this work provide guiding principles for the selection of process variables for keyhole-mode laser welding of aluminum alloys, depending on the particular application and the criteria for acceptable level of defects.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".