Predicting the Elastic Modulus of Hybrid Fibre Reinforced Thermoplastics
Bibliographic record
Abstract
Hybrid fibre reinforced thermoplastics (containing more than one type of fibre) offer several design possibilities that do not exist with single fibre reinforced systems. Although there are extensive experimental data available in the literature on a variety of hybrid systems, a reliable and simple analytical model to predict the stiffness of these composites is not available. In this study, a modification of the hybrid rule of mixtures equation is developed to determine the stiffness properties of hybrid composites. Experimental data from single fibre reinforced thermoplastics forms the basis of the modification. Combinations of E-glass, hemp and hardwood flour were blended into high-density polyethylene in total fibre loadings of 10 to 60wt% to produce hybrid composites. The density and Young's modulus of the hybrid composites fell between the extreme values obtained for the single fibre composites. The modified rule of hybrid mixtures equation was found to adequately predict the tensile modulus of the hybrid composites.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".