Grain Refinement Of Magnesium and Az19E Magnesium Alloy By Addition Of MgB2 Inoculant
Bibliographic record
Abstract
The increased use of magnesium alloys with improved mechanical properties is a prominent strategy towards increasing fuel efficiency of vehicles and decreasing emissions. This study investigates the grain refining efficiency and fading of MgB2 micro- and nano-particle added Pure Mg and AZ91E. Addition of micro and nano-sized MgB2 provided a reduction in grain size for Pure Mg and AZ91E. Enhanced heterogeneous nucleation and grain growth restriction was believed to be the source of refinement. Fading was observed for both Pure Mg and AZ91E, with the nano-particle added castings showing an increased resistance. The elongation of the Pure Mg samples showed improvements, whereas no improvements for UTS and YS was seen. The improved ductility was believed to be due to the grain refinement and coefficient of thermal expansion mismatch. The AZ91E samples did not show improvements in mechanical properties. This was believed to be due to stress concentration from Al-Mn intermetallics.
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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".