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Record W2461992771 · doi:10.1177/0021998316650273

An experimental technique to characterize interply void formation in unidirectional prepregs

2016· article· en· W2461992771 on OpenAlexafffund
Rhena Helmus, James Kratz, Kevin Potter, Pascal Hubert, R. Hinterhölzl

Bibliographic record

VenueJournal of Composite Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicInjection Molding Process and Properties
Canadian institutionsMcGill University
FundersTechnische Universität MünchenUniversity of BristolMcGill UniversityDeutsche ForschungsgemeinschaftEngineering and Physical Sciences Research CouncilUniversity of Southern California
KeywordsMaterials scienceComposite materialVoid (composites)VaporizationContact angleMoistureAutoclaveGroove (engineering)Enhanced Data Rates for GSM EvolutionContact regionContact areaLayer (electronics)

Abstract

fetched live from OpenAlex

Out-of-autoclave prepreg processing requires evacuation of volatiles in the early stages of processing to achieve an acceptable final void content. In this study, single prepreg plies were laid-up onto a glass tool to simulate a ply–ply interface, to gain an understanding of initial air entrapment and eventual removal mechanisms. The contact was recorded during processing with various edge breathing configurations to identify the relationship between evacuation pathways and contact evolution. The existence of preferential flow channels along the fibre direction of the material was demonstrated by characterizing the prepreg surface. Gas evacuation in those channels prevented contact during an extended ambient temperature vacuum hold. The contact between the prepreg and glass tool equilibrated around 80% during the ambient vacuum hold, and reached full contact at elevated temperature after a brief loss in contact due to moisture vaporization, when the resin pressure decreased to below the water vapour pressure.

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 score0.003

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 head0.013
GPT teacher head0.245
Teacher spread0.232 · 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

Citations23
Published2016
Admission routes2
Has abstractyes

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