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Record W3139038642 · doi:10.1177/00219983211002247

Warpage investigation of carbon/PEEK discontinuous long fibre thin panels

2021· article· en· W3139038642 on OpenAlexaff
Caroline Collins, Natássia Lona Batista, Pascal Hubert

Bibliographic record

VenueJournal of Composite Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials sciencePeekComposite materialShrinkageStiffnessPolymer

Abstract

fetched live from OpenAlex

Discontinuous Long Fibre (DLF) composites, composed of randomly-oriented strands of chopped unidirectional pre-impregnated tape, have been used in the aerospace industry to produce intricate, net-shape parts with complex features – replacing complicated metallic brackets with single, lightweight parts. Carbon/PEEK DLF composites suffer from warpage problems driven by several factors including their high processing temperatures and semi-crystalline matrix shrinkage. This work aims to characterize warpage of thin-gauge parts and pursue mitigation. Results showed that the magnitude of warpage reduces with decreasing strand size and increasing thickness. At thicknesses greater than 2 mm, warpage appeared relatively stable. The introduction of ribbed features was explored as a mean of mitigating warpage by increasing part stiffness. No significant impact on the magnitude of warpage was observed within parts. However, the addition of ribs helped to control the warped shape of the part.

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 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 score0.013
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.228
Teacher spread0.212 · 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.

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
Published2021
Admission routes1
Has abstractyes

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