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Record W2982177041 · doi:10.3934/matersci.2019.6.985

Mechanical properties of sisal-epoxy composites as functions of fiber-to-epoxy ratio

2019· article· en· W2982177041 on OpenAlexaff
Araya Abera Betelie, Anthony N. Sinclair, Mark T. Kortschot, Yanxi Li, Daniel Tilahun Redda

Bibliographic record

VenueAIMS Materials Science · 2019
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSISALEpoxyMaterials scienceComposite materialUltimate tensile strengthFlexural strengthFiberIzod impact strength testFabricationNatural fiber

Abstract

fetched live from OpenAlex

Low density, low cost, environmental compatibility, wide availability, and high mechanical performance of raw materials are some considerable advantages of natural fiber composites. Sisal, very common type of natural fiber, is abundantly available in Ethiopia. This research aims to investigate mechanical properties of sisal reinforced composites such as tensile, flexural and impact strength. Fabrication of samples used the hand lay-up process with 15, 25, 30, 35, and 40 wt% sisal fiber to epoxy ratio. Tests for the properties indicated were made using the instron material testing system. Test results demonstrated, among the samples, that 30 wt% of sisal fiber-reinforced composites have the maximum tensile and flexural strength of 85.5 MPa and 85.79 MPa respectively. The impact strength has been found to be maximum for 40 wt% sisal fiber which is 24.5 kJ/m<sup>2</sup>. As the result show, and compared with other researcher findings, the mechanical properties are acceptable as substitutes for applications demanding low-cost engineering applications such as automotive internal parts including interior door panel, back seat and body panels.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0030.000

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 head0.014
GPT teacher head0.240
Teacher spread0.226 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations52
Published2019
Admission routes1
Has abstractyes

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