A Uniform Viscoelastic-Plastic Constitutive Model for MD-PMMA at a Wide Temperature Range
Bibliographic record
Abstract
The deformation characteristics of MD-PMMA vary greatly at different temperatures. In the paper, whether a uniform model could be used to describe these complex characteristics was discussed. Tensile properties of MD-PMMA at the temperatures of -50˚C, -25˚C, 20˚C, 60˚C, 90˚C were experimentally investigated. The entire deformation processes of PMMA were divided into four stages: elastic stage, viscoelastic stage, yielding stage and post-yielding stage. Strain softening and strain hardening phenomenon occurred in the yielding and post-yielding stage, it was the results of the competition between loading rate and plastic strain rate. A nonlinear model of activation dashpot was constructed, in the model, the evolution rate of plastic deformation was defined by Eyring’s theory, and the actual stress was the difference between external applied stress and internal resistance stress caused by plastic strain. The above activation dashpot serially connected with the standard linear model (SLM) to identify elastic and viscoelastic characteristics. A two iterations integral algorithm was proposed to simplify the inter-coupling between the internal stress and the plastic strain, and the unknown parameters in the model could be easily fitted by the experimental data. This uniform viscoelastic-plastic model was demonstrated that could predict different deformation behaviors at a wide temperature range.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".