Characterization of Polyethylene Using a New Test Method Based on Stress Response to Relaxation and Recovery
Bibliographic record
Abstract
A novel multi-relaxation-recovery (RR) test was proposed based on cyclic stages of stress relaxation and stress recovery. Three nonlinear visco-elastic models, that is, the standard model and two models with two dashpots connected either in parallel or in series, were examined for the analysis of the test results. Each model contains a time-dependent, viscous branch and a time-independent, quasi-static branch. The examination suggests that the standard model can determine the long-term, load-carrying performance of polyethylene (PE) and identify a transition point for the onset of plastic deformation in the crystalline phase, but the models with two dashpots connected either in parallel or in series are needed to provide a close simulation of the experimentally measured stress response in both relaxation and recovery stages of the RR test. In this work, the mechanical performance of two PEs was compared based on RR test results at room temperature. The RR tests were also conducted at elevated temperatures to explore the possibility of quantifying the activation energies for deformation of the dashpots at the relaxation stage. It was found the RR test has the advantage of separating the time-dependent and time-independent components of stiffness of the materials. The study concludes that the RR test can provide data for determining parameters in Eyring's model in order to characterize the contribution of time-dependent and time-independent components of the stress response to PE's deformation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".