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Record W2981946423 · doi:10.1177/0021998319883934

Experimental study of void evolution in partially impregnated prepregs

2019· article· en· W2981946423 on OpenAlexaff
Leyla Farhang, Mohammad Mohseni, Navid Zobeiry, G. Fernlund

Bibliographic record

VenueJournal of Composite Materials · 2019
Typearticle
Languageen
FieldEngineering
TopicEpoxy Resin Curing Processes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceComposite materialCompactionPorosityVoid (composites)EpoxyComposite numberInterconnectivityComposite laminatesAir permeability specific surface

Abstract

fetched live from OpenAlex

Controlling voids to minimize the final porosity level is an important concern when processing advanced composite structures. In this study, the porosity evolution during processing of partially impregnated prepregs is investigated using interrupted cure cycles and optical microscopy. Laminates made of MTM 45-1/5HS carbon/epoxy prepreg subjected to different cure cycles, bagging conditions, and humidity levels were studied. Fiber tow geometry and gas permeability were measured to determine the amount of compaction and the interconnectivity of unsaturated zones in the laminates. Three types of voids were identified: inter-laminar, fiber tow and resin voids, all with different origins and evolution patterns. It is shown that gas transport (both in-plane and through-thickness), fiber bed compaction, and resin infiltration govern void evolution during processing. The results provide insights for development of representative transport models and to optimize processing cycles.

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 score0.002

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 integrity0.0000.000
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 head0.008
GPT teacher head0.247
Teacher spread0.239 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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