Neural networks approach for characterisation of viscoelastic polymers
Bibliographic record
Abstract
Abstract Recent developments in computer‐aided polymer processing have brought along the need for accurate description of the behaviour of viscoelastic materials under the conjugated effect of applied stress and temperature. In order to serve this purpose, in this study, experimental data provided by bubble inflation tests for flat circular thermoplastic membranes (CTPM) comprised of viscoelastic materials when subjected to combined effects of applied stress and temperature are coupled with numerical simulations to obtain the required parameters for the characterisation of such materials. First, pressure inside the bubble and height at the hemispheric pole are recorded during bubble inflation experiment. Thereafter, Christensen's theory of viscoelasticity is employed to define the constitutive model of flat circular thermoplastic membranes (CTPM) and nonlinear equilibrium equations of the inflation process are solved using finite difference method with deferred corrections. As a last step, a neuronal algorithm (ANN model) is employed to minimise the difference between calculated and measured parameters to determine material constants for Christensen viscoelastic model. Although the developed procedure can be applied to several polymeric materials, in this paper, this technique is successfully implemented for high‐impact polystyrene (HIPS) and acrylonitrile–butadiene–styrene (ABS), at typical thermoforming temperatures, 150 and 145°C, respectively. Using these coefficients, the material behaviours of HIPS and ABS with Christensen's constitutive law are reproduced. The material model obtained in this study for HIPS and ABS can be implemented into industrial and academic softwares for applications and design purposes.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".