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Mechanical Properties of Uncured Thermoset Tow Prepreg: Experiment and Finite Element Analysis

2023· preprint· en· W4382197635 on OpenAlexafffund
Mina Derakhshani Dastjerdi, Massiomo Carboni, Mehdi Hojjati

Bibliographic record

VenuePreprints.org · 2023
Typepreprint
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWavinessMaterials scienceComposite materialThermosetting polymerUltimate tensile strengthTensile testingPlain weaveFinite element methodSlippageEpoxyFiberStiffnessModulusStress (linguistics)YarnStructural engineering

Abstract

fetched live from OpenAlex

In this paper, the tensile behavior of unidirectional carbon/epoxy prepreg is experimentally analyzed at varying loads and temperatures, focusing on the considerable nonlinearity at the beginning of the stress-strain curve. The high viscosity of this material presented difficulties in securely holding specimens during tensile testing. As a result, modifications were made to the conventional gripping method to enable the acquisition of reliable test data. Additionally, a longer gauge length was chosen to minimize the impact of slippage on the elastic modulus measurement. To support the experiment, a micromechanical model of a prepreg tow with fiber waviness is proposed. An RVE model of periodically distributed unidirectional waved cylindrical fibers embedded within the matrix is developed to predict the effective material stiffness parameters. Numerical results are presented for different amplitude-to-wavelength ratios which indicate the fiber waviness reduces the tensile modulus of the composite. The simulation results are in good agreement with the uniaxial tensile test of the prepreg tow.

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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.156
GPT teacher head0.325
Teacher spread0.169 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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Same venuePreprints.orgSame topicMechanical Behavior of CompositesFrench-language works237,207