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Record W4295953428 · doi:10.4050/f-0078-2022-1154

Air Entrapment Prediction in Composite Manufacturing

2022· article· en· W4295953428 on OpenAlexaff
Michael West, Félix Nguyen, Yih-Farn Chen, Benjamin Cournoyer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsFinite element methodComposite numberMaterials scienceComposite materialCompactionMechanical engineeringStructural engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

Rotorcraft are highly engineered systems designed to perform missions in extreme locations around the world. To accommodate harsh environments and non-trivial cyclical loading conditions, components are commonly fabricated using advanced materials including composites. Dynamic load bearing thick laminate components are particularly challenging to produce due to the potential for entrapped air to be present within a fully cured component. A proof-of-concept model was developed to predict entrapped air and out-gassing. Thermo-chemical and flow-compaction finite element analyses were completed using a combination of commercial-off-the-shelf software and a custom proof-of-concept model developed by Convergent Manufacturing Technologies, Inc. Predicted results were compared against defects observed within laminated composites fabricated in a laboratory environment. Initial finite element predictions agreed moderately well with entrapped air observations.

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 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 categoriesInsufficient payload (model declined to judge)
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 score0.606
Threshold uncertainty score0.999

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)0.0020.000

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 head0.194
Teacher spread0.186 · 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 teacher head, not a consensus.

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
Published2022
Admission routes1
Has abstractyes

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