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Record W2328557635 · doi:10.2514/6.2012-1568

Scaling Challenges Encountered with Out-of-Autoclave Prepregs

2012· article· en· W2328557635 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEpoxy Resin Curing Processes
Canadian institutionsMcGill University
Fundersnot available
KeywordsScalingAutoclaveMaterials scienceMechanical engineeringEngineeringForensic engineeringMetallurgyMathematics

Abstract

fetched live from OpenAlex

The traditional manufacturing method for flight-critical aerospace structures made of composite materials is the autoclave. Autoclave processing is robust and well-understood, but involves high acquisition and operation costs. Out-of-autoclave materials and techniques are increasingly considered as cost-effective replacements to autoclaves; however, their capacity to accommodate scale-up issues commonly encountered when manufacturing larger parts have not yet been thoroughly investigated. The present study considers two such issues for a representative out-of-autoclave prepreg: the effects of resin out-time at room temperature and the material’s ability to evacuate entrapped air. Room-temperature outtime is shown to affect the resin flow phenomena that occur during processing and lead to dramatic increases in tow porosity; however, different temperature cure cycles are shown to mitigate this issue. The material’s permeability is shown to be adequate in-plane but practically non-existent through-thickness in the as-received condition; however, modifications are shown to increase this permeability to acceptable levels and consequently reduce porosity.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score0.377

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 science0.0000.000
Research integrity0.0000.000
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 head0.035
GPT teacher head0.242
Teacher spread0.206 · 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

Quick stats

Citations4
Published2012
Admission routes1
Has abstractyes

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