Quantification of dynamic mixing performance of single screws of different configurations by visualization and image analysis
Bibliographic record
Abstract
Abstract The visualization and image analysis techniques developed by the authors for the study of quantifying the dynamic quality of mixing of a single‐screw extruder were employed to investigate the mixing performance of screws of different configurations. The mixing quality was quantified by calculating the variances of the light intensity of the bitmap files cropped from the films taken at the screw length of 15D and 17D of a 45‐mm screw diameter single‐screw extruder. Temperature and screw speed were the two variables studied. While temperature and back pressure were found to have little effect on the mixing quality, screw speed was demonstrated to be a major factor responsible for the mixing fluctuation observed during extrusion. In fact, screw speed and mixing fluctuation appeared to have a relationship that the higher the screw speed, the bigger the fluctuation (i.e., poorer quality of mixing). The experimental results also revealed that the secondary flight of a barrier screw might be essential to improve the quality of mixing. © 2009 Wiley Periodicals, Inc. Adv Polym Techn 28:1–15, 2009; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/adv.20142
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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 | 0.001 | 0.001 |
| 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".