Modelling Behaviour of PET for Stretch and Micro-Blow Moulding Applications Using an Elasto-Visco-Plastic Material Model
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Bibliographic record
Abstract
Abstract Polyethylene terephthalate (PET) has been widely used in the stretch blow moulding (SBM) process for packaging applications. Finite element analysis has become extensively useful for assessing container designs and enabling the designers to perform analyses earlier in the design cycle to determine the best material and the best structure. However, there are several challenging issues due to various processing parameters and complex material behaviour, which is both temperature and strain-rate dependent. In this paper, we generalize the G'Sell-Jonas law in the three-dimensional (3D) case to model and simulate the elasto-visco-plastic (EVP) behaviour of PET, taking into account strain-hardening and strain-softening. In addition, it is observed that the internal pressure (inside the preform) is significantly different from the nominal pressure (imposed in the blowing device upstream) since the internal pressure and the enclosed volume of the preform are fully coupled. In order to accurately simulate this phenomenon, a thermodynamic model was used to characterize the pressure-volume relationship (PVR). The predicted pressure evolution is therefore more realistic when imposing only the machine power of the blowing device (air compressor or vacuum pump). Mechanical and temperature equilibrium equations are fully nonlinear and solved separately with implicit schemes on the current deformed configuration, which is updated at each time step. Biaxial characterization tests were used to determine the model parameters in order to simulate the SBM process using the PVR. Three industrial case studies, comparing simulated thickness predictions to experimental measurements, will be presented in order to illustrate the applicability of the proposed model.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it