A study of grain refinement of AZ91E and Mg-9 wt.% AI alloys using zinc oxide
Bibliographic record
Abstract
Grain refinement is a proven method to improve mechanical properties of Mg alloys. In this research, the influence of ZnO on the microstructure of selected magnesium alloys was investigated. For graphite mold casting with an addition of 0.75 wt. % ZnO, the grain size of the AZ91E alloy decreased from 217 μm to 108 μm. For the binary alloy (Mg-9 wt.% Al), the grain size reduced from 288 μm to 93 μm with an addition of 3 wt.% ZnO. No significant fading of ZnO grain refiner was observed for both the alloys. In permanent mold casting process, with an addition of 0.5 wt.% ZnO, the grain size of the AZ91E alloy decreased from 133μm to 79 μm with significant improvements in mechanical properties. Cleavage type fracture was dominant in the base alloy while alloys refined with 0.5 wt.% ZnO showed more quasi-cleavage type fracture.
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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) | 0.000 | 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".