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Record W2791477137 · doi:10.1177/0021998318762295

Integration of resin flow and stress development in process modelling of composites: Part I – Isotropic formulation

2018· article· en· W2791477137 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.
fundA Canadian funder is recorded on the work.

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
KeywordsThermosetting polymerMaterials scienceComposite materialFinite element methodIsotropyResidual stressStress (linguistics)CompactionPlane stressStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Process modelling of thermoset matrix composites is typically divided into two distinct and sequential steps: (i) flow-compaction analysis before gelation of the resin when it behaves in a fluid-like manner, and (ii) thermomechanical analysis after gelation when appreciable modulus and thereby stress development occurs. This two-part paper presents a novel approach to integrate the simulation of resin flow and stress development seamlessly during the processing of composites. Part I lays the theoretical foundation for the simpler case of isotropic materials while Part II extends the methodology to the case of transversely isotropic materials. The formulation is implemented in a 2D plane strain finite element code written in MATLAB. Relevant numerical examples are presented to demonstrate both aspects of flow-compaction and stress development throughout the curing process of thermoset matrix composite materials. The effects of resin flow on the development and the final values of residual stresses are investigated.

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)0.000
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 score0.182
Threshold uncertainty score0.381

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)0.0000.000

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 head0.025
GPT teacher head0.252
Teacher spread0.226 · 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