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Record W3081523664 · doi:10.1002/pen.25513

Viscoelastic characterization of high‐density polyethylene membranes under the combined effect of the temperature and the gravity for thermoforming applications

2020· article· en· W3081523664 on OpenAlexaff
N. Ben Aoun, Fouad Erchiqui, Hatem Mrad, G. Dituba‐Ngoma, François Godard

Bibliographic record

VenuePolymer Engineering and Science · 2020
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsViscoelasticityThermoformingMaterials scienceHigh-density polyethyleneViscosityComposite materialArrhenius equationCharacterization (materials science)ThermalSilsesquioxaneMechanicsPolyethyleneThermodynamicsPolymerClassical mechanicsNanotechnologyKinetics

Abstract

fetched live from OpenAlex

Abstract Numerical simulation of the heating stage of thermoplastics in thermoforming requires a good knowledge of the behavior of the materials used. To this end, a study is being conducted on the characterization of the viscoelastic behavior of a circular membrane, made of high‐density polyethylene (HDPE), under the combined effect of temperature and the force of gravity. The experimental tests were carried out in a convection oven for five temperatures (100, 110, 120, 130 and 140°C). For the numerical characterization of the viscoelastic behavior, two viscoelastic models were considered: the classic Kelvin‐Voigt model and the new three‐parameter modified Burger's model (Jeffrey model) that we propose. The mechanical parameters of both models were identified using the Levenberg‐Marquardt algorithm. The thermal‐dependency of the viscosity was characterized by two thermal models: the Arrhenius law and the William‐Landel‐Ferry (WLF) equation.

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)0.000
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 score0.351
Threshold uncertainty score0.191

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 studies0.0000.001
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 head0.003
GPT teacher head0.186
Teacher spread0.183 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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