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Record W4404064422 · doi:10.3390/applmech5040043

Tensile Properties of 3D-Printed Jute-Reinforced Composites via Stereolithography

2024· article· en· W4404064422 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied Mechanics · 2024
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsStereolithographyComposite materialUltimate tensile strengthMaterials science3d printedEngineeringManufacturing engineering

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.188
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it