Latest developments in BlowView Optimization Package: application for multilayer, extrusion blow molded plastic fuel tanks
Bibliographic record
Abstract
The aim of this work is to show both the latest developments and capabilities of BlowDesign, the design optimization package of the BlowView software, to improve the extrusion blow molding process for the manufacturing of multilayer plastic fuel tanks (PFT). Based on gradient optimization methodology, the BlowDesign software loops over the finite element simulation software BlowView. The design optimization scheme in BlowDesign for the manufacturing of blow moulded multilayer fuel tanks uses two main consecutive optimization steps: die shaping geometry and processing conditions optimization. The die shaping optimization consists of manipulating the geometry of the bushing-mandrel shape in order to distribute the material uniformly around the inflated part using static flexible deformable ring (SFDR) die technology. The process optimization consists of manipulating design variables such as the extrusion flow rate, extrusion time, pre-blow pressure and the die gap programming profile (programming points) to minimize the part thickness variance around the desired thickness distribution or to minimize the part weight subject to a minimum thickness constraint based on client performance requirement criteria using the next die technologies: vertical wall distribution system (VWDS), partial wall distribution system (PWDS) or die slide motion (DSM). In this work, the proposed optimization approach will be investigated on a specific Jerry Can and PFT. During the former optimization, a barrier layer thickness optimization is performed simultaneously to minimize the hydrocarbon permeation through the PFT wall to satisfy the daily emission imposed by government regulation. The optimization has proven to be an excellent tool to improve the design by decreasing the objective function and satisfying the process constraints over optimization iterations.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".