Optimization of static and impact mechanical properties for Kenaf-Coir hybrid composite modified with carbon nanotube (CNT)
Bibliographic record
Abstract
This decade has observed an upsurge in the eco-friendly materials because of the development of composites using natural fibers. These composites are made from renewable resources and are gaining popularity for their high performance in engineering applications. Industries are increasingly interested in using materials that are sustainable and resource-efficient. This research proposes a new innovative hybrid composite developed using coir and kenaf fibers, carbon nanotubes acting as a nanofiller, and a matrix made up of epoxy resin, detailing how they are fabricated, tested, and optimized based on different weight percentages. The weight percentages considered for CNT nanoparticles are 0, 1, 2, and 3 wt.%, coir, and kenaf fibers are considered in weight percentages of 12, 13, 14, and 15, whereas thickness is regarded as 2,3,4 and 5 mm. This research evaluates the mechanical features of this hybrid composite fabricated using a vacuum bag molding process. The different composite samples are tested using mechanical tests and subsequently optimized using the design of experiment (i.e., Taguchi method) and analysis of variance (ANOVA) method to arbitrate the best weight percent combination of the innovative hybrid composite. On the basis of the optimization results, the best composite sample obtained includes, 3 wt% of CNT, 15 wt% of kenaf, 15 wt% of Coir, and 4mm thickness of the sample, as it yields the highest tensile modulus and strength among all the hybrid composite samples. The outcomes from the research indicate that the hybridization of kenaf fibers into coir fibers, along with CNTs as fillers in the hybrid composite has enhanced the overall tensile strength, and flexural strength of the hybrid composite in comparison to the coir composite and kenaf composite alone, depicting the superiority of natural fiber hybrid composite over synthetic fiber hybrid composite.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".