Effect of Si Level on the Evolution of Zr‐Bearing Dispersoids and the Related Hot Deformation and Recrystallization Behaviors in Al–Si–Mg 6xxx Alloys
Bibliographic record
Abstract
The precipitation behavior of Zr‐bearing dispersoids is investigated in Al–Si–Mg 6xxx alloys with different Si levels (0.4, 0.7, and 1.0 wt%) at three homogenization temperatures (450, 500, and 550 °C). The hot deformation behavior is studied using uniaxial compression tests at different Zener–Hollomon parameters. The microstructure evolution during hot deformation and postdeformation annealing is evaluated using the electron backscatter diffraction technique. The results show a significant influence of the Si level and homogenization temperature on the precipitation of two types of Zr‐bearing dispersoids. Si promotes the precipitation of both spherical L12–Al3Zr and elongated DO22–(Al,Si)3(Zr,Ti) dispersoids during low‐temperature homogenization. However, it accelerates the transformation of Zr dispersoids from L12 to DO22 at high homogenization temperature. The flow stress is more influenced by the solid solution level and hot deformation parameters rather than by the dispersoid distribution. The fine dense L12–Al3Zr dispersoids provide higher recrystallization resistance during postdeformation annealing compared with the large elongated DO22–(Al,Si)3(Zr,Ti) dispersoids. Owing to the uniform distribution of dispersoids and limited dispersoid‐free zones, the high Si alloy (1.0%) exhibits best recrystallization resistance among the three alloys studied.
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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".