Tensile Properties of 3D-Printed Jute-Reinforced Composites via Stereolithography
Bibliographic record
Abstract
This paper investigates the tensile properties of jute-reinforced composites fabricated using stereolithography (SLA) 3D printing. Tensile tests were conducted using dog-bone tensile specimens following ASTM D638 Type IV specifications. Additionally, the study explores the effect of layer thickness on the tensile properties of the 3D-printed composite material, examining four different layer thicknesses: 0.025 mm, 0.05 mm, 0.075 mm, and 0.1 mm. The findings revealed that the tensile strength of the 3D-printed jute-reinforced composites increased with the printing layer thickness, reaching its maximum at a layer thickness of 0.1 mm. This represents an enhancement of approximately 84% compared to pure resin. Examination of the fiber–matrix interface under an optical microscope revealed a wavy pattern, suggesting that the interface may act as a mechanical interlock under tensile loads, thereby significantly enhancing tensile strength. The strength of the 3D-printed jute-reinforced composites was found to be comparable to that of glass fiber mat epoxy composites. This demonstrates that 3D SLA-printed jute-reinforced composites offer a promising avenue for producing next-generation composites that are typically challenging to manufacture using traditional fabrication techniques.
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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".