MétaCan
Menu
Back to cohort
Record W2177461088 · doi:10.1177/0040517515581582

Mechanical behavior of airbag fabrics under quasi-static loading: an experimental evaluation of macro- and meso-scopic properties

2015· article· en· W2177461088 on OpenAlex
S. Zacharski, Frank Ko, Reza Vaziri

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

VenueTextile Research Journal · 2015
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceComposite materialAirbagCrimpYarnNonlinear systemStructural engineeringCoatingEngineering

Abstract

fetched live from OpenAlex

In this paper the current generation of 350 dtex airbag coated and uncoated fabrics are examined experimentally under a multitude of simple and complex deformations. The geometric dimensions of the fabric architecture and the load–elongation behavior of the yarn constituents are studied. Furthermore, deformational shear behavior of airbag fabrics, which have not previously been investigated, are examined here. The stress–strain behavior of the yarn as well as the fabrics with and without coating are found to be highly nonlinear. Under uniaxial loading, nonlinearities of the fabric occur at lower strains due to crimp of the fabric, whereas under biaxial loading, the nonlinearity occurs at low and intermediate strain levels resulting from a combination of the inherent nonlinear material response of the yarn and geometric changes in the fabric structure. The data generated not only provide the basis for structural analysis of the airbag, but can also be used to develop more sophisticated definitions of the constitutive behavior of the fabric.

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)0.011
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.419
GPT teacher head0.475
Teacher spread0.057 · 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