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Record W2604325304 · doi:10.1002/nme.5555

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

2017· article· en· W2604325304 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

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.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.372
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.038
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