Numerical simulation of the wire‐pinning process in PET film casting: Steady‐state results
Bibliographic record
Abstract
Abstract Poly(ethylene terephthalate) (PET) film casting involves melt flow through a slot die, across a small span, and onto a fast moving quench drum. In the “wire‐pinning” casting process, a thin electrified wire close to the line of contact with the drum creates a strong pinning force that delays air entrainment to higher line speeds. Nonuniform wetting of the die lips by the extruded melt is thought to be responsible for the formation of streaks, a defect in the machine direction. A finite element model of the film casting process with wire pinning was developed to assist in understanding what causes significant wetting of the lips and whether this can be avoided by electrostatic pinning. The solution of the governing equations provide the location of the static and dynamic contact lines, thus finding the wetting and pinning points in the process. The simulations investigate the sensitivity of the static wetting line locations on the die lands to the imposed values of static contact angle and die‐lip gap. It was found that while the contact angle has a small effect on the extent of die‐lip wetting within the parameter ranges examined, there is a considerable chance that the feed slot surfaces can be dewetted. This seems to be the greatest danger for causing streaks rather than excessive wetting of the die lands. © 2011 American Institute of Chemical Engineers AIChE J, 2012
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".