Numerical modelling of complex parison and sheet formation in blow molding processes using BlowView Software
Bibliographic record
Abstract
BlowView is an engineering 2.5D finite element simulation software, developed at NRC, dedicated to simulate conventional extrusion blow molding, twin-sheet extrusion blow molding, stretch blow molding and thermoforming processes. This versatile blow molding simulation software is highly automated, flexible and user-friendly, yet allows users in-depth analysis capabilities for a wide range of materials, including optimization and permeability. Extrusion blow molding, and twin-sheet extrusion blow molding, are extensively used in manufacturing automotive plastic fuel tanks (PFT). These processes consist of three main phases: parison/sheet formation, inflation and part cooling and solidification. The parison/sheet formation is the most critical stage, as the final dimensions and mechanical performance of the PFT is directly related to the initial extrudate shape, which often requires the use of advanced die shaping technologies such as: Vertical Wall Distribution System (VWDS), Partial Wall Distribution System (PWDS), Die Slide Motion (DSM), and/or a combination of all three. These technologies are all available in the BlowView software, and can be handled simultaniously and synchronized with the machine programming points. In order to predict the extrusion with sag and swell, BlowView uses a hybrid approach that couples fluid mechanics to represent the die flow, with solid mechanics to represent the parison/sheet behavior outside the die, and a phenomenological swell model to capture the die geometry effect. This approach permits avoiding instability issues encountered by traditional fluid mechanics, especially at high Weissenberg numbers. After extrusion, the parison/sheet inflation is predicted tacking into account all mold components and their respective position and displacements. The modelling capabilities of NRC’s BlowView software will be presented based on using an industrial case study of a PFT. Permeability and optimization results will be also illustrated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".