Aging behavior of flexcast® Al-Mg alloys with Sc and Zr additions
Bibliographic record
Abstract
AA5xxx series Al alloys have presented great potential in recent decades for packaging and automotive applications. A recent further development of these alloys has been the addition of Sc and Zr. The presence of these elements is known to develop strengthening phases that also influence other metallurgical phenomena such as recovery and recrystallization. However, the low solubility of both Sc and Zr in Al requires high cooling rates during casting in order to attain a supersaturated solid solution which conventional DC casting is unable to achieve. In this study, two Al- 3%Mg alloys, one Sc-free and the other containing 0.4% Sc were cast using the Flexcaster® available at the Novelis Global Technology Center (Kingston, Ontario). The Flexcaster® is a strip casting technology that transforms liquid aluminum into a directly rollable cast ingot that is not subject to scalping or homogenization processing. This technology is characterized by relatively high solidification and cooling rates. Aging experiments were performed on as-cast and cold-rolled samples of the two alloys to study the evolution of strengthening phases and their impact on recovery and recrystallization. Both alloys were artificially aged following casting at 200, 300 and 400°C for times ranging from 30s to 72 hours. The alloys were characterized by hardness and tensile tests and electrical resistivity measurements as well as optical and electron microscopy in order to monitor the aging behavior of the alloys. Results show enhanced strengthening in the Sc-containing alloy and superior high temperature microstructural stability in comparison to the Sc-free alloy as well as conventionally cast AlMgSc alloys.
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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".