Preform shape and operating condition optimization for the stretch blow molding process
Bibliographic record
Abstract
Abstract In this work, a new design approach was developed to automatically and consecutively predict optimal preform geometry and optimal operating conditions for the stretch blow molding process. The numerical approach combines a constrained gradient‐based optimization algorithm that iterates automatically over predictive finite element software. The strategy allows for targeting a specified container thickness distribution by manipulating consecutively the preform geometry (thickness and shape) and the operating parameters subject to process and design constraints. For the preform shape optimization, the preform geometry is mathematically parameterized for simplified treatment and the corresponding sensitivities are evaluated using a finite difference technique. A finite difference technique is also employed for the operating condition optimization. The constrained optimization algorithms are solved via the use of the sequential quadratic programming method that updates the design variables accordingly. Predicted optimization results obtained on an industrial case are presented and discussed to assess the validity when compared to experimental results and the robustness of the proposed approach. POLYM. ENG. SCI., 47:289–301, 2007. © 2007 Society of Plastics Engineers.
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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) | 0.000 | 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".