Numerical and Experimental Analyses of Hybrid Composites Made from Amazonian Natural Fibers
Why this work is in the frame
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Bibliographic record
Abstract
The application of lignocellulosic fibers as reinforcements in composite materials has found increasing use in recent years, due to the attractive characteristics of natural fibers such as their low cost, high specific modulus, biodegradability, abundance and with many technical qualities. Natural fiber hybrid composites are very frequently used in automotive aerospace and other industries. In this work, numerical and experimental analysis is carried out to compare curauá, jute and sisal fibers in epoxy composites for use in industry. The most appropriate hybridization effect by establishing the amounts of each fiber on the mechanical properties was considered. Finite Element Models were designed and validated through mechanical tests. The number of Finite Element models and specimens performed was determined through the design of experiments using the Taguchi Method and then the results were statistically validated. Higher strength was obtained in composites made with curauá fiber, followed by jute and sisal fibers. Such behavior was achieved by FEM and experimental tests, revealing an increase in tensile strength by increasing the amount of fibers up to 35% in total. Higher strength was achieved when the composite was made with curauá (20 wt.%), jute (10 wt.%) and sisal (5 wt.%) fibers. The results show a good agreement between the FEM and the experimental tests. Furthermore, the results of the present study were compared with those obtained previously mentioned in the open literature.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it