Ultrasonic techniques for imaging and measurements in molten aluminum
Bibliographic record
Abstract
In order to achieve net shape forming, processing of aluminum (Al) in the molten state is often necessary. However, few sensors and techniques have been reported in the literature due to difficulties associated with molten Al, such as high temperature, corrosiveness, and opaqueness. In this paper, development of ultrasonic techniques for imaging and measurements in molten Al using buffer rods operated at 10 MHz is presented. The probing end of the buffer rod, having a flat surface or an ultrasonic lens, was immersed into molten Al while the other end with an ultrasonic transducer was air-cooled to room temperature. An ultrasonic image of a character "N", engraved on a stainless steel plate immersed in molten Al, and its corrosion have been observed at 780 degrees C using the focused probe in ultrasonic pulse-echo mode. Because cleanliness of molten Al is crucial for part manufacturing and recycling in Al processing, inclusion detection experiments also were carried out using the nonfocused probe in pitch-catch and pulse-echo modes. Backscattered ultrasonic signals from manually added silicon carbide particles, with an average diameter of 50 microm, in molten Al have been successfully observed at 780 degrees C. For optimal image quality, the spatial resolution of the focused probe was crucial, and the high signal-to-noise ratio of the nonfocused probe was the prime factor responsible for the inclusion detection sensitivity using backscattered ultrasonic signals. In addition, it was found that ultrasound could provide an alternative method for evaluating the degree of wetting between a solid material and a molten metal. Our experimental results showed that there was no ultrasonic coupling at the interface between an alumina rod and molten Al up to 1000 degrees C; therefore, no wetting existed at this interface. Also because ultrasonic velocity in alumina is temperature dependent, this rod proved to be able to be used as an in-line temperature monitoring sensor under 1000 degrees C in molten Al.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".