Characterization of loading, relaxation, and recovery behaviors of high‐density polyethylene using a three‐branch spring‐dashpot model
Bibliographic record
Abstract
Abstract This paper presents an analysis of the stress evolution of high‐density polyethylene (HDPE) at loading, relaxation, and recovery stages in a multi‐relaxation‐recovery (RR) test. The analysis is based on a three‐branch spring‐dashpot model that uses the Eyring's law to govern the viscous behavior. The spring‐dashpot model comprises two viscous branches to represent the short‐ and long‐term time‐dependent stress responses to deformation, and a quasi‐static branch to represent the time‐independent stress response. A fast numerical analysis framework based on genetic algorithms was developed to determine values for the model parameters so that the difference between the simulation and the experimental data could be less than 0.08 MPa. Using this approach, values of the model parameters were determined as functions of deformation and time so that the model can simulate the stress response at loading, relaxation, and recovery stages of the RR test. The simulation also generated 10 sets of model parameter values to examine their consistency. The study concludes that the three‐branch model can serve as a suitable tool for analyzing the mechanical properties of HDPE, and values for the model parameters can potentially be used to characterize the difference among PEs for their mechanical performance. Highlights Developed computer programs to determine parameter values automatically. Explained the unusual stress drop during stress recovery after unloading. Evaluated the statistical range of the parameter values for the good fitting.
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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 | 0.001 |
| 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".