Temperature-Stress Equivalency in Nonlinear Viscoelastic Creep Characterization of Thermoplastic/Agro-fiber Composites
Bibliographic record
Abstract
The viscoelastic characterization of agro-filler based plastic composites is of paramount importance for the material’s long-term commercial success. To predict creep, it is important to derive a relationship between deformation, time, temperature, relative humidity, and stress. Since temperature shift can interfere with stress shift in creep, the predictive model should incorporate the relationship between these two shifts. Rice husk-HDPE beams were subjected to creep and recovery in the flexural mode and stress/time/temperature-related creep behavior of the same was studied. Temperature-related creep constants and shift factors were determined for the material and the constants were compared against theoretical two-phase constants. The combined effect of temperature and stress on creep strain was accommodated in a single analytical function where the interaction was shown to be additive. This means that the stress equivalency of temperature is possible. This constitutive equation can predict creep in the long run. Although stress dependency is nonlinear, temperature dependency is linear and thermorheologically complex. The ‘single-phase’ material behavior (creep constants) was also compared with a ‘two-phase’ predictive model, where the creep constants were estimated with the ‘theory of mixtures’.
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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) | 0.000 | 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 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".