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Record W2594553451 · doi:10.5267/j.esm.2017.3.002

Analysis of mechanical properties of jute fiber strengthened epoxy/polyester composites

2017· article· en· W2594553451 on OpenAlexvenueno aff
Prabhakar Kaushik, Jaivir Jaivir, Kapil Mittal

Bibliographic record

VenueEngineering Solid Mechanics · 2017
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsnot available
Fundersnot available
KeywordsComposite materialMaterials scienceEpoxyPolyesterFiber

Abstract

fetched live from OpenAlex

In recent years, efforts have been made to produce advanced composite materials in order to lessen environmental impact and to extent sustainability. Traditional materials are largely substituted by composites due to their greater properties like flexural strength, low thermal expansion and high strength. Numerous studies are present that show the process of composite materials reinforcement with natural fiber to improve mechanical and thermal properties. The vital aspect of exploitation of natural fiber in composites is associated with biodegradability. An extensive range of different natural fibers has been used for reinforcement till now. In present work, mechanical properties of jute fiber reinforced epoxy and polyester composites manufactured using Taguchi optimization method are investigated, experimentally. It was found that jute reinforced epoxy composite had better mechanical properties than jute polyester composite. Also, Epoxy-jute composite had lower erosion wear rate than polyester jute composites.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.238
Teacher spread0.223 · 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

Citations42
Published2017
Admission routes1
Has abstractyes

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