Ultrasonic Processing of Magnesium Alloy for Property Enhancement
Bibliographic record
Abstract
The reduction of harmful greenhouse gas (GHG) emissions can be realized by utilizing lightweight structural metals, such as magnesium. Magnesium alloys have the potential to replace higher-density aluminum and ferrous components in automotive and aerospace industries, thereby decreasing vehicle weight and the associated fuel requirements. However, their strength and ductility must be improved to ensure widespread application. This goal can be achieved through ultrasonic processing in the molten state, a technique that is gaining popularity in the manufacturing of light alloys. In this study, the effects of high-intensity ultrasonic vibration on the microstructure and hardness of AZ91E Mg alloy was investigated. The molten alloys were subjected to sonication of varying durations, and the resulting castings were characterized using optical microscopy, scanning electron microscopy and hardness testing. Sonication was found to successfully increase the hardness of the alloy relative to the base condition. This improvement was attributed to the refinement of the magnesium grain structure as well as the Mg17Al12 and Mn-Al secondary phases in the sonicated alloys. The competitiveness of magnesium alloys can be significantly enhanced via ultrasonic processing, offering important opportunities for the production of greener, light metal components.
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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".