The influence of alloying on recrystallization behavior and texture development of Mg-(Ca, Zn) alloys
Bibliographic record
Abstract
The influence of alloying Mg with Ca and Zn on the microstructural and texture evolution of binary Mg-2Zn (wt.%) and Mg-0.5Ca (wt.%) during static recrystallization (SRX) was systematically quantified using in situ techniques. In situ heating experiments were conducted inside of a scanning electron microscopy (SEM) equipped with an electron backscatter diffraction (EBSD) detector and a heating stage to track the nucleation and growth of strain-free grains throughout the entire annealing process. This enabled the sequential mapping of specific regions of interest throughout the recovery and recrystallization processes. In Mg-2Zn, deformation was accommodated mainly through extension { 10 1 ¯ 2 } twinning and { 10 1 ¯ 1 − 10 1 ¯ 2 } and { 10 1 ¯ 3 − 10 1 ¯ 2 } double twinning. These twin interfaces and twin nucleation sites along grain boundaries served as preferential sites for the nucleation of recrystallized grains. Additionally, the deformed texture was retained even after recrystallization due to the new grains inheriting the orientation of the twinned grains. In Mg-0.5Ca, multiple twinning systems were activated such as extension { 10 1 ¯ 2 } twinning, { 10 1 ¯ 1 } and { 10 1 ¯ 3 } contraction twinning, and { 10 1 ¯ 1 − 10 1 ¯ 2 } and { 10 1 ¯ 3 − 10 1 ¯ 2 } double twinning. Contraction and double twins served as preferred nucleation sites for recrystallized grains due to the higher strain energy generated within these regions. With more nucleation sites available within the Mg-0.5Ca microstructure, the resulting recrystallized crystallographic orientation exhibited a weaker texture compared to Mg-2Zn.
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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".