Application of the “Theory of Mixtures” to Temperature – Stress Equivalency in Nonlinear Creep of Thermoplastic/Agro-fibre Composites
Bibliographic record
Abstract
The viscoelastic characterization of agro-filler based plastic composites is of paramount importance for these materials' long-term commercial success. To predict creep, it is imperative to derive a relationship between deformation, time, temperature, and stress. This work is the harbinger in modelling of the nonlinear creep behaviour of two-phase materials, where an extended “theory of mixtures” has been used to describe all the creep related parameters. The stress- and temperature-related shift factors were estimated in terms of the activation energy of the constituents. 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. The model was validated under rigorous conditions and is unique because it describes creep not through curve fittings, but in terms of the creep constants of the constituents. This constitutive model is not only a vanguard in the prediction of long term creep of many biocomposites but also in the modelling of creep under step loading of temperature.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".