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Record W2590210705 · doi:10.1177/0021998317695421

Development of a generalized analytical model for tubular braided-architecture composites

2017· article· en· W2590210705 on OpenAlexaff
Garrett W. Melenka, Jason P. Carey

Bibliographic record

VenueJournal of Composite Materials · 2017
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBraidMaterials scienceFinite element methodDiamondYarnComposite materialStiffnessStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Tubular braided composites are manufactured using a Maypole braiding machine which interlaces yarns in order to manufacture a braided structure. Braids can be produced in Diamond (1/1), Regular (2/2) and Hercules (3/3) patterns. In addition, axial yarns can be included in order to produce triaxial braid structures. Several analytical and finite element analysis models have been developed in order to predict the elastic properties of braided composites. Despite the fact that many models exist for braided composites, a comprehensive model has not been presented that can capture the variety of braiding patterns which can be manufactured using the braiding process. In this study, a new analytical model has been developed that can describe the elastic properties of Diamond, Regular and Hercules braids. The proposed analytical model uses a volume averaging stiffness method in order to account for yarn undulations and the orientation of braid yarns within the braid structure. The model presented here has been compared with the existing FEA and analytical models and has also been validated experimentally. Experimental validation comprised tensile and torsional tests in order to predict the longitudinal and shear moduli for both Diamond and Regular braid geometries. The experimental and proposed model results highlight the effect of braiding pattern and braiding angle on the mechanical properties.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.037
GPT teacher head0.292
Teacher spread0.255 · 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.

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

Citations29
Published2017
Admission routes1
Has abstractyes

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