Melting Quality of Polymers in Internal Mixer Diagnosed by Ultrasound
Bibliographic record
Abstract
Abstract Real-time, non-intrusive and non-destructive ultrasonic technology has been used to monitor the melting process in an internal mixer. Visual observation, mechanical torque measurement, and ultrasonic signatures, such as amplitude and time delay of transmission and reflection echoes were used for the diagnosis of the melting process of low density polyethylene (LDPE). Phenomena during the melting process, including phase change from solid to melt, partially melted pellets, air bubbles inside the melt, were successfully monitored by ultrasound. The ultrasonic signatures were able to determine when the polymer has melted completely. The method of moving standard deviation (MSD) was applied to establish the melting completion timing accurately. Higher temperature of mixing chamber and faster rotation speed of blades reduced melting completion period, indicated by MSD of ultrasonic signatures. The presented ultrasonic technique can be utilized to optimize the melting process.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".