Ultrasonic in‐line monitoring of polymer extrusion
Bibliographic record
Abstract
Abstract In‐line ultrasonic monitoring of polymer co‐extrusion and twin‐screw extrusion are presented. Co‐extrusion of high density polyethylene (HDPE) and a thermoplastic elastomer based on polypropylene‐EPDM (ethylene‐propylene‐diene monomer) has been investigated by ultrasonic sensors consisting of piezoelectric transducers and clad buffer rods. One extremity of the rod (probing end) was installed flush with the die surface so as not to disturb the material flow. The other end was air cooled in order to protect the transducer from excessive heating. This approach has been demonstrated to be quite convenient for monitoring and controlling industrial material processes: first, it can work at temperatures up to 1000°C; second, the clad buffer rod probing end can be machined to the same shape as those of commercial temperature and pressure sensors commonly used in the extrusion process. Therefore, no modifications are required for the installation in the original equipment. The information obtained includes the position of the interface between polymers and the stability of the process. The same ultrasonic probe has also been installed on a barrel of a twin‐screw extruder. This study was performed using polyethylene and polystyrene. It has been verified that the ultrasonic sensor can be successfully operated along the extruder screw and that the ultrasound can give access to the material properties while the polymer is being processed. This means that the technique can be exploited to monitor and control in situ the characteristics of the polymer being transformed in operations typically performed on twinscrew extruders, such as compounding, visbreaking or reactive extrusion.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".