Elastic properties evaluation of banana-hemp fiber-based hybrid composite with nano-titanium oxide filler: Analytical and Simulation Study
Bibliographic record
Abstract
In recent years, nano-filler-based hybrid composites have gained significant attention from the research community; The nano-filler-based hybrid composites can have potential applications in numerous sectors. Nano-fillers are bringing a leading development in material science and natural fibers-based composites. The present study considers the impact of various weight percentages of nano-titanium oxide (NTiO2) fillers (2%, 4%, and 6%) on the elastic features of novel hybridized banana-hemp fiber-reinforced epoxy composites. The proposed composite is analyzed for its elastic properties like longitudinal and transverse elastic modulus, axial Poisson's ratio, and axial shear modulus using homogenized micromechanical models, namely, Mori-Tanaka (M-T) model, Generalized self-consistent (G-SC) model and Modified Halpin-Tsai (M-HTS) model. The composite is modeled using one layer of banana fiber, one layer of NTiO2 and epoxy, and one layer of hemp fiber. All three layers of the composite are arranged in the sequence of banana fiber at 450, a layer of NTiO2 and epoxy at 00, and hemp fiber at 450. The proposed composite's vector sum deformation and strength are examined by employing the ANSYS APDL application. The results obtained in this study are compared with the experimental work mentioned in the literature. The composite reinforced with six weight% NTiO2 has the highest mechanical strength, and the modified Halpin-Tsai (M-HTS) model is the most effective in calculating the elastic features of the proposed composite. In addition to the above, the hybridization effect for the proposed composite is also estimated to analyze the tensile failure strain of banana and hemp fiber in the proposed hybrid composite structure.
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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 | 0.001 | 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) | 0.000 | 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".