MétaCan
Menu
Back to cohort
Record W2604325304 · doi:10.1002/nme.5555

Image‐based model reconstruction and meshing of woven reinforcements in composites

2017· article· en· W2604325304 on OpenAlexaff
Anna Madra, Piotr Breitkopf, Alain Rassineux, F. Trochu

Bibliographic record

VenueInternational Journal for Numerical Methods in Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsDiscretizationPolygon meshCurvatureFiberMaterials scienceComposite materialWoven fabricGeometryMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

Summary A method based on dual kriging is proposed to process X‐ray microtomographic scans of textile composites in order to construct a 3D representation of the fiber architecture with a regulated level of details. The geometry is optimized by using the curvature energy of fiber tow profiles in order to determine the best discretization scheme; then the nugget effect is applied in kriging to smooth the outward surface of fiber tows. This approach allows creating 3D models of variable resolution ranging from the X‐ray scan level to geometric representations with surface meshes required for numerical simulation. The method is applied to a glass fiber textile laminate embedded in a thermoplastic matrix, and preliminary results for the estimation of the local permeability of the fiber tows are presented. Copyright © 2017 John Wiley & Sons, Ltd.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.038
GPT teacher head0.387
Teacher spread0.349 · 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 designSimulation or modeling
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

Citations22
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal for Numerical Methods in EngineeringSame topicMechanical Behavior of CompositesFrench-language works237,207