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Record W1972762399 · doi:10.1115/imece2002-32312

Mechanical Modeling of Fabrics in Bending

2002· article· en· W1972762399 on OpenAlexaff
Timothy John Lahey, G. R. Heppler

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSlippingCoulomb frictionBendingNonlinear systemHysteresisJammingMaterials scienceCompression (physics)CoulombMechanicsInertial frame of referenceStructural engineeringComposite materialClassical mechanicsPhysicsEngineeringCondensed matter physics

Abstract

fetched live from OpenAlex

A model of fabric bending that includes a nonlinear elastic contribution, a viscous friction contribution, a Coulomb friction contribution, and a hysteretic contribution is presented. These are combined to recover the loading, unloading and hysteresis behaviors observed in the bending tests performed under the Kawabata Evaluation System. Results of the model and its components are compared and contrasted with experimental results. It is found that inertial effects dominate the behavior of the model in the early stages of the KES test and that, once the static friction threshold is overcome, friction arises from the slipping of the yarns with respect to each other. The results show that nonlinear elastic behavior arises from jamming of the yarns and their subsequent compression.

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 score0.868
Threshold uncertainty score0.993

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)0.0080.000

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 head0.098
GPT teacher head0.289
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 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

Citations3
Published2002
Admission routes1
Has abstractyes

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