In-Situ, Laser-Ultrasonic Monitoring of the Recrystallization of Aluminum Alloys
Bibliographic record
Abstract
Laser-ultrasonics is a non-destructive and non-contact technique to generate and detect ultrasound in materials. The measured ultrasonic velocity depends on the orientation distribution of the crystallites and may be used to infer the lowest order texture coefficients. Recrystallization generally involves texture changes and can thus be monitored using ultrasonics. In this paper, cold-rolled samples of an Al-Mg alloy (AA5754) and an Al-Si-Mg-Cu alloy (AA6111) are annealed in a Gleeble thermomechanical simulator at various temperatures. The recrystallization kinetics is monitored in-situ and in real time by laser-ultrasonics. It is found that the longitudinal and shear velocity variations correlate well with the recrystallized fraction, as evaluated by metallography and by the softening behaviour of samples submitted to similar thermal cycles. it is also found that the ultrasonic behaviour is consistent with the randomization of texture and a reduction of the W400 and W420 texture coefficients.
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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 | 0.000 | 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".