Development of TTMSP to Predict the Performance of Discontinuous Natural Fibre-Polymer Composites
Bibliographic record
Abstract
Natural fiber-polymer composites are becoming increasingly important for load-bearing application due to their unique benefit of light weight and good mechanical performance.Polymers alone exhibit time dependent mechanical properties influenced by many environmental factors.More significantly, when natural fibres are present in the polymer matrices, the thermomechanical properties of such composites are changed by the environmental conditions and exhibit an internal and a global time dependent behavior.While durability analysis of the natural fibre-polymer composites is the main concern in real-life application, development of prediction methods is essential for evaluating their mechanical performance.Both temperature and moisture diffusion are known to influence the time dependent mechanical properties of polymer based composites; however, conventional analysis method considers the temperature factor only.Therefore, an evaluation technique is presented in this paper to describe the nonlinear visco-elastic behavior of natural fibre-polymer composites under concurrent temperature and moisture diffusion effects.Using an analogous approach to the TTSP, time-moisture superposition will be used to identify the moisturedependent shift factors under an assumption that the effects of temperature and moisture can be decoupled and separately determined, and reassembled later on.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".