Effects of sonication amplitude on the microstructure and mechanical properties of AZ91E magnesium alloy
Bibliographic record
Abstract
Light metals are gaining increased attention due to ecological sustainability concerns and strict emission regulations. Magnesium (Mg) is one such metal that has the potential to replace high density components, which can reduce emissions through lightweighting. However, the mechanical properties of Mg alloys must be improved for them to become viable candidates for structural applications. To this end, the current study examines the effect of sonication vibrational amplitude on the microstructure and mechanical properties of AZ91E Mg alloy. The molten alloys were subjected to ultrasonic treatment at a frequency of 20 kHz, 180 s of processing time and vibrational amplitudes ranging from 1.25 to 15 µm. The resultant castings were characterized using optical microscopy, scanning electron microscopy and tensile testing. It was found that sonication with amplitudes up to 7.5 µm was able to effectively refine the secondary phases of the alloy. Similar trends were observed for grain size and yield strength. The refinement in microstructure was likely caused by the finer grain size and cavitation induced undercooling of the liquid metal. In addition, it was also noted that even the lowest level of amplitude (1.25 µm) was able to increase the density, improve the ultimate tensile strength and ductility of the castings. The tensile strength and ductility were thought to have been enhanced by ultrasonic degassing and refinement in microstructure, while the yield strength was improved through the Hall-Petch effect. The results from this study provided a basis for optimizing the sonication process and promoting its use in industry. As a result, Mg alloys improved through ultrasonic processing have the potential to replace higher density components, with consequent energy efficiency and environmental and ecological benefits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".