Grain Refinement of Az91e Magnesium Alloy Through Ultrasonic Treatment, Inoculation and Hybrid Processing for Improvements to Microstructure and Mechanical Properties
Bibliographic record
Abstract
Light metals are gaining increased attention due to ecological sustainability concerns and consequent strict emission regulations. Magnesium (Mg) has the potential to replace aluminum (Al) and steel components, which can reduce emissions through lightweighting. However, widespread implementation of Mg is hindered by its low mechanical properties. As a means for improving the mechanical properties of cast alloys, ultrasonic processing has shown increasing promise within the past years. As well, grain refinement and reinforcement with ceramic particles can also improve the properties of cast alloys. This dissertation examines the effects of ultrasonic treatment (UST), grain refinement with α-Al 2 p3rticles, and their combination to produce hybrid refined castings on the microstructure and mechanical properties of cast Mg alloys. To evaluate the effects of UST, the molten alloys were subjected to 60 to 240 s of sonication at a frequency and amplitude of 20 kHz and 15 µm, respectively. Vibrational amplitudes ranging from 1.25 to 15 µm were also investigated. Sonication time was found to improve the mechanical properties of the alloy relative to the base condition. In addition, vibrational amplitudes up to 7.5 µm were able to refine the secondary phases of the alloy. In both cases, the improvement was attributed to the refinement of the Mg grain structure and secondary phases. Grain refinement with Al2O3 was performed with mechanical stirring (MS) and addition levels ranging from 0.25 to 2 wt.%. The highest levels of improvement for ultimate tensile strength and ductility were achieved through 0.5 wt.% Al2O3 addition. Moreover, hybrid refined samples were prepared by combining 1 wt.% Al2O3 addition and UST. The hybrid samples displayed improved mechanical properties relative to the base alloy and the samples prepared with MS. This was attributed to the finer grain size, and enhanced inoculant distribution. Thus, significantly improved mechanical properties of Mg alloys are facilitated by UST, grain refinement with Al2O3 and hybrid processing. These processes have the potential to replace higher density Al and iron-based components, with consequent energy efficiency and environmental and ecological benefits.
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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".