Numerical/Experimental Study of Process Optimization Conducted on an Al-Cu Alloy Produced by Combined Fields of Applied Pressure and Ultrasonic Vibration
Bibliographic record
Abstract
Single-pressure and ultrasonic action have their own unique advantages in the treatment of metal melts. When the two are combined into a composite field, the advantages of a single physical field can be fully utilized. So, the cavitation and acoustic streaming effects characteristics of an Al-5.0Cu alloy treated under different coupling process parameters, related to applied extrusion pressure and ultrasonic vibration, were analyzed by combining numerical simulation and experimental verification. The simulation results were experimentally verified by quantitative analysis of the microstructure, the melt, and its macro characteristics. The results show that the closing time t decreases with an increase in the extrusion pressure. In addition, when the ultrasonic power and extrusion force are increased simultaneously (100 MPa and 1 kW), the average grain size and the proportion of columnar grains reach the ideal effect. The influence of pressure parameters is greater, which will also lead to an increase in the proportion of columnar crystals. By optimizing the parameters, the grain size can be further reduced, the proportion of columnar crystal structure can be reduced, and fine and uniform equiaxed crystal structures can be easily obtained.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".