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Record W2341503681 · doi:10.14288/1.0072867

Gas transport and water vapourization in out-of-autoclave prepreg laminates

2013· article· en· W2341503681 on OpenAlexaff
Kevin Hsiao

Bibliographic record

VenuecIRcle (University of British Columbia) · 2013
Typearticle
Languageen
FieldEngineering
TopicStructural Analysis of Composite Materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAutoclaveMaterials scienceComposite materialMetallurgy

Abstract

fetched live from OpenAlex

Primary load-bearing aerospace structures have traditionally been manufactured using autoclave processing. However, recent advances in material technology have led to the development of pre-impregnated (prepreg) composites that are designed to be cured out-of-autoclave (OOA) and can potentially reduce the costs of processing. In OOA processing, voids are removed by vacuum evacuation through gas pathways in the prepreg. The availability and interconnectivity of these pathways determine the prepreg’s gas transport ability. Voids enter into the prepreg via moisture absorption during storage and physical air entrapment during manufacturing and material handling. Environmental effects, such as relative humidity, can alter the moisture content of prepregs and lead to significant vapour generation during cure. This study examines the gas transport and water vapourization characteristics of OOA prepreg CYCOM 5320/T650 (epoxy/carbon). Gas permeabilities in the in-plane and through-thickness directions are measured, and the effects of processing history (debulk time, temperature etc.) on gas transport are examined. The relationships between relative humidity, moisture content, and vapour generation are analyzed, and the use of mass flow sensors for water vapour quantification is validated. Gas transport is shown to be highly anisotropic in CYCOM 5320/T650, with inplane gas permeability being three orders of magnitude greater than through thickness. Processing history has a significant effect on permeability, with extended debulking sessions reducing in-plane permeability by 50%. Increasing temperature causes in-plane and through-thickness permeabilities to fall. Microscopy analyses reveal that permeability change is a result of collapsing voids and resin flow during heating. Moisture content is relatively unaffected by relative humidity until reaching 30% RH, after which increases in moisture content become more apparent. Vapourization of absorbed moisture between 0 %RH and ambient conditioned vacuum bagged laminates are similar, suggesting that under typical process conditions moisture vapourization mainly comes from the vacuum bag consumables. During vacuum bag processing, vapourization of absorbed moisture occurs immediately upon heating, peaks around 40 to 60oC, and then dissipates as heating reaches the hold temperature. The mass flow sensors are demonstrated to be capable of detecting the onset and termination of moisture vapourization and quantifying the total mass of water vapourized to within 10% error.

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 score0.003
Threshold uncertainty score0.006

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 head0.004
GPT teacher head0.145
Teacher spread0.141 · 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

Citations8
Published2013
Admission routes1
Has abstractyes

Explore more

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