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Stacking Sequence and Weight Fraction Effect on Tensile and Flexural Properties of Woven Jute and Woven Carbon-Jute Reinforced Polyester Composites

2024· article· en· W4394879258 on OpenAlexaff
Md. Kharshiduzzaman, Sakib Hossain Khan, Mack Jerald Rozario, Golam Fahim, Shahnewaz Bhuiyan, M. A. Gafur

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2024
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsCarleton University
Fundersnot available
KeywordsComposite materialMaterials scienceStackingFlexural strengthUltimate tensile strengthComposite numberWoven fabricFlexural modulusFiberPolymerCarbon fibersChemistry

Abstract

fetched live from OpenAlex

Abstract Hybrid composites are a category of composites in which more than one types of fiber are used to reinforce the matrix. In this work, the mechanical properties of jute woven & and carbon-jute woven reinforced polymer matrix hybrid composites were evaluated to assess a comparative study between the two configurations varying the stacking sequences. Specimens of composites were prepared by hand layup process. To observe the effect of fiber weight fraction on properties, two types of composites were made having matrix-fiber weight fractions of 85:15 & and 80:20. Moreover, by altering the stacking sequences of the composites, these properties were also examined for the carbon-jute reinforced polymer matrix composites. It was observed that a hybrid jute-carbon composite having a stacking sequence of j/c/j/c/j and weight fraction ratio of 80:20 exhibited a better tensile strength of 108.795(10.885) MPa and Young’s modulus of 6.052(0.489) GPa. Superior flexural strength of 150.41 (±7.501) MPa and flexural modulus of 6.845(±0.825) GPA were found in hybrid jute-carbon composite having stacking sequence of j/c/j/c/j/c/j/c/j that has a weight fraction ratio of 80:20. In both cases, better mechanical properties were found for hybrid composites with higher fiber content and having alternate stacking sequences.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
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 head0.014
GPT teacher head0.221
Teacher spread0.207 · 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 teacher head, not a consensus.

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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

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