Study of the Mechanical Properties of Bamboo and Glass Fiber Reinforced Hybrid Polymer Matrix Composites
Bibliographic record
Abstract
Bamboo and glass fibers reinforced hybrid epoxy matrix composites were produced by stir casting method and the effects of production parameters (temperature, drying time, particle concentration, and bamboo-to-glass fiber ratio) on the mechanical properties were investigated. Overall optimum fabrication parameters are a bamboo fiber drying temperature of 25°C, a drying time of 20 mins, a particle size of 0.75 mm, and a bamboo-to-glass fiber ratio of 1:3. An in-depth analysis of the results shows that the improvement of different composite properties can be specifically targeted using different parameter combinations. Specifically, the shortest fibers below 0.75 mm, dried at room temperature for 60 minutes, and a bamboo-glass fiber ratio of 1:3 yielded the highest tensile strength of 7.47 MPa, which represents about 120 % increase as compared to unreinforced epoxy. Similarly, the longest fibers between 1 to 2.8 mm, dried at 110°C for 60 minutes, and a bamboo-glass fiber ratio of 1:1, exhibited the highest impact energy of 37.2 J, corresponding to about 79 % improvement. Moreover, the medium length fibers between 0.75 and 1 mm, dried at 80°C for 60 minutes, and a bamboo-glass fiber ratio of 3:1, exhibited the highest hardness of 16.73 HV, translating to 37% increase. Sample S8, which is the hybrid reinforced composite containing 0.75 mm bamboo fiber-particle size exhibited the lowest wear rate of 1.09 g/Nm implying the highest wear resistance. All these show the effectiveness of the mixture of bamboo and glass fibers in improving the mechanical properties of the composites. However, excessive bamboo concentration led to a significant reduction in wear properties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".