The Role of the Nd/Zn Ratio on the Stability of Mg-Zn-Nd Clusters and the Evolution of Texture in Two Mg-Zn-Nd Alloys during Annealing
Bibliographic record
Abstract
Texture evolution in two magnesium alloys, Mg-4%Zn-1%Nd and Mg-1%Zn-1%Nd (weight percentage), was studied after rolling and the subsequent isothermal annealing. The finish rolling was completed in a single pass with a thickness reduction rate of ~30% at 100 °C and a rolling speed of 1000 m/min. After cooling to room temperature, the rolled samples were annealed at 350 °C for different annealing times. Upon annealing, the maximum intensity of the basal pole texture decreases as recrystallization progresses. In the Mg-1Zn-1Nd alloy (with a high Nd/Zn ratio), texture weakening is maintained even after complete recrystallization and grain coarsening, while in the Mg-4Zn-1Nd alloy, texture strengthening occurs after grain coarsening, and a single peak replaces the double split basal peaks. In the Mg-1Zn-1Nd alloy, grain coarsening is accompanied by a bimodal grain size distribution, whereas in the Mg-4Zn-1Nd alloy, the grain coarsening leads to a uniform grain size distribution. TEM investigations show the formation of the Zn and Nd rich clusters at early stage of annealing in both alloys. During recrystallization, these clusters were dissolved in the Mg-4Zn-1Nd alloy, but they are more stable in the Mg-1Zn-1Nd alloy. In our opinion, the formation of these stable clusters is one of the main factors for texture weakening of the Mg-Zn-RE 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".