Ultrasonic temperature determination during industrial materials processing
Bibliographic record
Abstract
Two technologies for measuring temperature with ultrasound during the processing of materials are presented. Both methods use clad buffer rods. These approaches present the great interest of being able of providing, in addition to the temperature measurement, the simultaneous monitoring of other parameters of the material being processed such as its elastic properties. The first method uses clad buffer rods with two steps machined at the probing end contacting the material being processed. The ultrasonic time delays in the two regions which are between the rod end and the first step, and between the first and the second step can be calibrated versus temperature variation. The measurement of these time delays during material processing, associated with a conduction heat transfer model provide an accurate temperature at the probing end. For molten zinc processing at temperature ranging between 350 and 700 °C, good agreement was obtained between the temperatures measured ultrasonically and those measured by a thermocouple. The second method is applied at the nozzle of a polymer injection-molding machine. The temperature measurement relies on the relationship between the pressure, temperature and velocity (PVT) of a specific polymer. Two clad buffer rods arranged in a transmission geometry are used to measure the averaged ultrasonic velocity in the molten polymer flowing within the nozzle. A reflection geometry using only one clad buffer rod can also be employed. The pressure is measured by using both a commercial pressure sensor and a clad buffer rod. The measured velocity and the pressure lead to the measurement of the average temperature in the molten polymer. For a polycarbonate, assuming the uncertainty in the measurement of pressure and ultrasonic velocity to be 1 MPa and 1 m/s, respectively, the temperature measurement uncertainty is then about 3 °C with a 0.5 °C.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".