MétaCan
Menu
Back to cohort
Record W3009934485

Moisture Effect on Properties of Out-of-Autoclave Laminates with Different Void Content

2020· article· en· W3009934485 on OpenAlexfundno aff
Afshin Bayatpour, Mehdi Hojjati

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2020
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialVoid (composites)MoistureAutoclaveWater contentFabricationComposite numberMetallurgy
DOInot available

Abstract

fetched live from OpenAlex

Fabrication of large structures using out-of-autoclave prepreg materials will lead to a great amount of savings in manufacturing costs. In the out-of-autoclave processing method, the presence of voids inside the laminate has been an issue due to the lack of high pressure during manufacturing. This study aims primarily to observe the moisture absorption response of composite samples containing different levels of void. By changing the vacuum level inside the bag during the manufacturing process, three different unidirectional laminates at three levels of void have been manufactured. After immersing the samples in warm water at 60°C for about one year, the moisture absorption level was monitored and then diffusion coefficients were calculated using Fick’s law. Results show that the moisture absorption coefficient changes by %8 within the experimental range of void contents. The mechanical behaviour of these laminates has been studied at four different moisture levels by performing dynamic mechanical analysis (DMA) and short beam shear tests. Empirical results indicate that, in general, interlaminar shear strength and glass transition temperature decrease by moisture build-up inside the samples. DiBenedetto equation is proposed to make a correlation between the moisture content and glass transition temperature.

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)0.000
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 score0.005
Threshold uncertainty score0.739

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 science0.0010.000
Research integrity0.0000.001
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 head0.046
GPT teacher head0.237
Teacher spread0.191 · 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.

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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueSpectrum Research Repository (Concordia University)Same topicMechanical Behavior of CompositesFrench-language works237,207