A study on grain refinement of AZ91E magnesium alloy with Al-5TiB₂, AI-Al₄C₃ and ZnO additions
Bibliographic record
Abstract
There is great interest in increasing the use of magnesium (Mg) alloys in transportation applications to reduce weight. The use of these alloys would increase if their strength and castability were improved. Through grain refinement, it is possible to achieve significant improvement in specific mechanical properties such as strength and hardness. For aluminum (A1)-containing Mg alloys, a commonly used grain refiner is hexachloroethane (C₂Cl₆). Though effective, C₂Cl₆ use releases harmful chlorinated hydrocarbons. It is therefore desired to find novel grain refiners that are effective and environmentally safe. This thesis focused on the grain refinement of AZ9lE alloy with three refiners: Al-5TiB₂, Al-A1₄C₃ and ZnO. The refiners were chosen due to their known grain refinement efficiency in low-Al Mg or Mg-Zn alloys. Castings with each refiner were made in graphite molds to establish i) the optimum addition levels to achieve the smallest average grain size and ii) the effect of holding time on fading of grain refinement efficiency. These castings ere used to collect thermal data and sectioned for microscopy and hardness testing. Castings were also made with the optimum parameters in a permanent mold specifically designed to investigate hot tearing susceptibility. The results indicated that all three additions enabled grain refinement of the base alloy, and no fading of grain refiner efficiency was observed. These refiners transformed the coarse dendritic microstructure in AZ9lE to one that was equiaxed and globular. At optimal levels, the refinement mechanism was heterogeneous nucleation. Also, hot tearing was significantly decreased with all refiners except for ZnO. The excess Zn from ZnO addition led to an increase in the freezing range, thus increasing the hot tear severity. The hardness of AZ9lE did not increase with ZnO addition as it did with the other two refiners.
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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".