Identification of cell‐nucleation mechanism in foam injection molding with gas‐counter pressure via mold visualization
Bibliographic record
Abstract
The mechanisms of cell nucleation and growth are investigated in foam injection molding (FIM) using gas‐counter pressure (GCP). An in‐situ mold visualization technique is employed. The application of GCP suppresses cell nucleation, and prevents the blowing agent from escaping during mold‐filling. The inherent structural heterogeneity in the regular FIM can be improved because of the uniform cavity pressure when employing GCP. The cavity pressure profiles show much faster pressure‐drop rates using GCP, because the single‐phase polymer/gas mixture has a lower compressibility than the two‐phase polymer/bubble mixture. Therefore, both the cell nucleation and growth rates are significantly increased through a higher pressure‐drop rate on the removal of the GCP. The effect of GCP magnitude on the cell morphology is explored. When the GCP is lower than the solubility pressure, bimodal foaming occurs. As the GCP increases above the solubility pressure, the cell density increases because of the higher pressure‐drop rate. © 2016 American Institute of Chemical Engineers AIChE J , 62: 4035–4046, 2016
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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 | 0.000 |
| 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 | 0.000 | 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".