Experimental investigation of tensile, flexural and hardness properties of polyester resin echinatus fiber reinforced composite material
Bibliographic record
Abstract
Nowadays, composites made of plant-based fibers may be used to polymer resin synthetic fiber reinforced composites since they are less expensive, renewable, abundant, less abrasive, and lightweight. Echinatus fiber obtained from the stem of the echinatus plant in an abundant amount. The main objective of this study was to develop echinatus polyester-resin fiber reinforced composite material and investigate its flexural strength, hardness and tensile strength. Echinatus plant was collected and echinatus fiber was extracted by the decortication process from the echinatus plants manually, and treated with 5% NaOH for the improvement of bond and interfacial shear strength. And then, the test specimens were manufactured using a mass fraction with 0 , 45 and 90 orientations using technique of hand lay-up. The tensile strength, hardness, and flexural strength were investigated using samples that were prepared according to the ASTM standards. 70% echinatus fiber to 30% polyester composite material with 0 orientation was found as the material with maximum tensile strength of 60.60 MPa, flexural strength of 96.8 MPa, and hardness values of 44.54 HRA. Considering the mechanical properties’ experimental results, echinatus fiber-reinforced composite with 70% fiber at 0 orientation can be good substitutes for synthetic materials.
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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) | 0.002 | 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".