Tensile Modulus Prediction of Glass Fiber/Stainless Steel Wire Mesh-Reinforced Hybrid Composites via Rule of Hybrid Mixtures
Bibliographic record
Abstract
Hybrid composites have been considered emerging materials that have garnered the attention of researchers around the globe. Combining two kinds of reinforcement may balance their merits and demerits in hybrid composites. In this work, glass fiber/wire mesh-reinforced epoxy composites were prepared via vacuum infusion to minimize void formation. Non-hybrid wire mesh and glass fiber-reinforced composites were also fabricated for comparison purposes. The thicknesses of all the composite laminates were fixed at 4 mm. Tensile tests were performed at a cross-head displacement rate of 2 mm/min with reference to ASTM D3039 to obtain the modulus of composite laminates. Subsequently, the tensile modulus of each composite laminate was predicted using the Rule of Hybrid Mixtures (RoHM). A comparison was made between the modulus of the composite laminates obtained from the tensile tests and prediction using RoHM. In accordance with the results obtained, it was found that the incorporation of glass fiber increased the modulus of the hybrid composites but did not significantly improve their tensile strength. The highest modulus (22.6 GPa) was obtained in non-hybrid glass fiber-reinforced composites, which is 107.71 % greater than non-hybrid wire mesh-reinforced composites. When comparing the experimental and predicted tensile modulus of the glass fiber/wire mesh composite laminates, both results matched well, demonstrating a linear increase in the tensile modulus with an increase in glass fiber content. Overall, the percentage error of the prediction was in the range of 3 – 6 %, indicating a high accuracy of the RoHM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".