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Record W2793207489 · doi:10.1177/0021998318762296

Integration of resin flow and stress development in process modelling of composites: Part II – Transversely isotropic formulation

2018· article· en· W2793207489 on OpenAlexafffund
S Mehdi Haghshenas, Reza Vaziri, Anoush Poursartip

Bibliographic record

VenueJournal of Composite Materials · 2018
Typearticle
Languageen
FieldEngineering
TopicEpoxy Resin Curing Processes
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransverse isotropyMaterials scienceIsotropyComposite materialVolume fractionStress (linguistics)Flow (mathematics)ViscoelasticityProcess (computing)Development (topology)Computer scienceGeometryMathematics

Abstract

fetched live from OpenAlex

Part I of this two-part paper presented the framework to integrate the simulation of resin flow and stress development during the manufacturing process of composites. In the current paper, the integrated approach developed in Part I for isotropic materials is extended to the case of transversely isotropic materials. Various numerical examples are considered in which the results obtained from the integrated approach are compared to those generated from the previously established models for processing induced stress development. These comparisons serve to elucidate the importance of accounting for the spatial and temporal variations in the resin volume fraction during processing and its effect on stress development. Such effects cannot be investigated in a non-integrated simulation environment where volume fraction variations due to resin flow have to be mapped sequentially from the flow simulation onto the next phase of the process simulation which treats the resin as an elastic or viscoelastic solid.

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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.026
GPT teacher head0.244
Teacher spread0.218 · 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

Citations8
Published2018
Admission routes2
Has abstractyes

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