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Record W3184032527 · doi:10.1080/15376494.2021.1952496

A master curve approach to model short- and long-term time-dependent nonlinear behavior of polyethylene

2021· article· en· W3184032527 on OpenAlexaff
André Kühl, Maria Anna Polak

Bibliographic record

VenueMechanics of Advanced Materials and Structures · 2021
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversity of Waterloo
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsCreepViscoplasticityViscoelasticityConstitutive equationNonlinear systemTerm (time)Materials sciencePower lawPhenomenological modelWork (physics)Stress (linguistics)MechanicsStructural engineeringMathematicsEngineeringMechanical engineeringComposite materialPhysicsFinite element methodStatistics

Abstract

fetched live from OpenAlex

This paper presents a new approach for modeling the constitutive behavior of time-dependent materials by applying two phenomenological constitutive models to creep curves. Data obtained from short-term test are used for predicting the long-term behavior. The nonlinear viscoelastic response is modeled by using the multi-Kelvin model and the viscoplastic behavior is described by a power law. The methodology proposed herein for obtaining the constitutive formulation is based on developing master curves. Results obtained for polyethylene are discussed and compared using creep tests at different stress levels and has proved to be reliable based on the results analyzed in this work.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.022
GPT teacher head0.254
Teacher spread0.231 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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