Ultrasonic processing of lightweight alloys: A critical review
Bibliographic record
Abstract
Ultrasonic processing in the liquid state has been identified as an effective method to improve the mechanical properties of Al and Mg alloys. Ultrasonic melt processing is capable of enhancing material properties through the application of high-frequency, high-power vibrations that form cavitation bubbles which pulsate and collapse throughout the melt volume. Thus, this technology has excellent potential in engineering high performance lightweight materials. With global trends converging toward greener energy, reduced greenhouse gas (GHG) emissions and increasingly stringent efficiency standards, lightweight and high-strength alloys such as aluminum (Al) and magnesium (Mg) are becoming an area of high interest. The aim of this review is to analyze the literature on ultrasonic processing of Al and Mg alloys in the last 15 years. This review discusses ultrasonic processing equipment, experimental set-ups, mechanisms of ultrasonic cavitation and acoustic streaming. As well, the effects of processing time, vibrational amplitude, and temperature on microstructure and properties are elucidated. Furthermore, it aims to investigate how a combination of sonication and particle reinforcement can affect the properties of Al and Mg alloys. The challenges of ultrasonic processing have been identified and expanded on in this review. This includes energy consumption, equipment complexity, temperature control, process optimization and limited industrial adoption.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".