MétaCan
Menu
Back to cohort

Locking and Stability of 3D Woven Composite Reinforcements

2014· article· en· W1989211416 on OpenAlexaff
Sylvain Mathieu, Philippe Boisse, Nahiène Hamila, F. Bouillon

Bibliographic record

VenueKey engineering materials · 2014
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsHyperelastic materialFinite element methodComposite numberMaterials scienceOrthotropic materialConstitutive equationStructural engineeringComposite laminatesHourglassComposite materialEngineering

Abstract

fetched live from OpenAlex

3D woven composite reinforcements preforming simulations are an unavoidable step of composite part processing. The present paper deals with thick composite fabric behavior modelling and issues arising during the numerical simulation of preforming. After the description of the independent deformation modes of initially orthotropic reinforcements, a physically motivated and invariant based hyperelastic strain energy density is introduced. This constitutive law is used to show the limitations of a classical finite element formulation in 3D fabric simulations. Tension locking is highlighted in bias extension tests and a reduced integration hexahedral finite element with specific physical hourglass stabilization is proposed. Instabilities due to the highly anisotropic behavior law, witnessed in bending dominated situations, are exposed and a stabilization procedure is initiated.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.010
GPT teacher head0.201
Teacher spread0.191 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueKey engineering materialsSame topicMechanical Behavior of CompositesFrench-language works237,207