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Record W4388635509 · doi:10.1080/09243046.2023.2279001

Experimental characterization of multiscale solidification in thermoset CFRP during gelation for flow and stress modeling

2023· article· en· W4388635509 on OpenAlexaff
Yuta Naito, Christophe Mobuchon, Anoush Poursartip, Masaaki NISHIKAWA, Masaki Hojo

Bibliographic record

VenueAdvanced Composite Materials · 2023
Typearticle
Languageen
FieldEngineering
TopicComposite Material Mechanics
Canadian institutionsUniversity of British Columbia
FundersCouncil for Science, Technology and Innovation
KeywordsMaterials scienceThermosetting polymerComposite materialRheologyPercolation (cognitive psychology)Dynamic mechanical analysisFlow (mathematics)Matrix (chemical analysis)ModulusShear (geology)PolymerMechanics

Abstract

fetched live from OpenAlex

Two types of flow mechanisms consisting of unidirectionally arrayed fibers and uncured thermoset resin exist in prepreg materials. These mechanisms are percolation flow where resin flows out of the gaps between fibers, and shear flow where resin and fibers flow together. Based on our previous study, we assumed that percolation flow is controlled by the rheology of the matrix resin, whereas shear flow is controlled by the rheology of prepreg. Based on this assumption, we experimentally evaluated the ‘multiscale’ solidification (solidification of matrix resin and that of prepreg) process using dynamic mechanical analysis during gelation. The solidification of matrix resin was examined by observing the development of loss angle, which provides a continuous description of the solidification process. On the other hand, the solidification of prepreg was characterized by analyzing the relationship between the shear storage modulus of prepreg and that of the matrix resin. Finally, we examined the difference in the solidification process between prepreg and matrix resin during gelation.

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 score0.005

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 integrity0.0000.001
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 head0.012
GPT teacher head0.235
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

Citations0
Published2023
Admission routes1
Has abstractyes

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