Numerical simulation of suction blow molding process for producing curved ducts
Bibliographic record
Abstract
During suction blow molding process, the extruded parison undergoes twisting deformation within the mold cavity, as the air drawing flow around the deforming parison exerts non‐uniform shear stresses on its surface. Such twisting deformation can compromise the specific radial and circumferential variations in parison thickness that are intentionally generated during extrusion. This research is devoted in developing a fluid–structure interaction model for predicting parison deformation during suction blow molding process, with a specific emphasis on the suction stage. A fluid flow model, based on Hele‐Shaw approximations, is formulated to simulate the air drag force exerted on the parison surface. The rheology of the material of the parison is assumed to obey the viscoelastic K‐BKZ model. As the suction process also involves the sliding of the parison within the mold cavity, a modified Coulomb's law of dry friction is used to simulate the frictional contact between parison and mold. The numerical results of this study allowed identifying a clear correlation between the twisting deformation undergone by the parison during the suction stage, also observed experimentally and the design parameters, namely, the air drawing speed, the geometry of the duct mold cavity, and the parison/mold eccentricity. POLYM. ENG. SCI., 59:418–434, 2019. © 2018 Her Majesty the Queen in Right of Canada
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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) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".