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Record W2809599553 · doi:10.1177/0021998318781226

Viscoelastic behavior of an epoxy resin during cure below the glass transition temperature: Characterization and modeling

2018· article· en· W2809599553 on OpenAlexaff
Alice Courtois, Martin Hirsekorn, María Benavente, Agathe Jaillon, Lionel Marcin, Édu Ruiz, Martin Lévesque

Bibliographic record

VenueJournal of Composite Materials · 2018
Typearticle
Languageen
FieldEngineering
TopicEpoxy Resin Curing Processes
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsEpoxyMaterials scienceViscoelasticityGlass transitionTime–temperature superpositionCreepComposite materialRelaxation (psychology)Superposition principleBendingConstitutive equationDegree (music)Dynamic mechanical analysisStress relaxationPolymerThermodynamicsFinite element method

Abstract

fetched live from OpenAlex

This paper presents a viscoelastic temperature- and degree-of-cure-dependent constitutive model for an epoxy resin. Multi-temperature relaxation tests on fully and partially cured rectangular epoxy specimens were conducted in a dynamic mechanical analysis apparatus with a three-point bending clamp. Master curves were constructed from the relaxation test results based on the time–temperature superposition hypothesis. The influence of the degree of cure was included through the cure-dependent glass transition temperature which was used as reference temperature for the shift factors. The model parameters were optimized by minimization of the differences between the model predictions and the experimental data. The model predictions were successfully validated against an independent creep-like strain history over which the temperature varied.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.009
GPT teacher head0.234
Teacher spread0.225 · 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 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

Citations64
Published2018
Admission routes1
Has abstractyes

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