A Hybrid Artificial Neural Network Approach for Modeling the Behavior of Polyethylene Terephthalate (PET) Under Conditions Applicable to Stretch Blow Molding
Bibliographic record
Abstract
Stretch blow molding (SBM) is widely utilized in industrial applications, yet the deformation characteristics of materials during this process are intricate and challenging to precisely articulate. To accurately forecast the stress-strain response of polyethylene terephthalate (PET) in SBM, a hybrid Artificial Neural Network (ANN)-based constitutive model has been developed. The model has been created by combining a physical-based function for capturing the small-strain behavior in parallel with an ANN-based model for capturing the temperature-dependent large-strain nonlinear viscoelastic behavior. The architecture of the ANN has been designed to ensure stability in a load-controlled scenario, thus making it suitable for use in FEA simulations of stretch blow molding. Data for training the model have been generated by a new semi-automatic experimental rig which is able to produce 850 stress-strain curves over a wide range of process conditions (temperature range 95-115 °C and strain rates ranging from 1/s to 100/s) directly from blowing preforms using a combination of high-speed video, digital image correlation and sensors for pressure and force. The model has already been implemented in the commercial FEA package Abaqus via a VUMAT subroutine, with its performance validated by comparing the prediction of the evolution of preform shape during blowing vs. high-speed images. In conclusion, the developed hybrid ANN model, when integrated into Abaqus, offers a more accurate simulation of SBM processes, contributing to improved design efficiency and product quality.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".