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Record W2062275003 · doi:10.1002/pen.20661

Micromechanical approach to modeling damage in crystalline polyethylene

2007· article· en· W2062275003 on OpenAlexaff
José A. Alvarado-Contreras, Maria Anna Polak, Alexander Penlidis

Bibliographic record

VenuePolymer Engineering and Science · 2007
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials sciencePolyethyleneViscoplasticityComposite materialMicrostructureConstitutive equationPolymerWork (physics)MechanicsStructural engineeringMechanical engineeringFinite element method

Abstract

fetched live from OpenAlex

Abstract The purpose of this article is to describe how the concepts of continuum damage mechanics can be applied to modeling of polyethylene materials under different loading conditions. The increasing use of polyethylene in diverse applications motivates the need for understanding how its molecular properties relate to the overall behavior of the material. Although microstructure and mechanical properties of polymers have been the subject of several studies, the irreversible microstructural rearrangements occurring at large deformations are not completely understood. In this work, a three‐dimensional damage constitutive model for polyethylene is proposed. The material is analyzed from a microscopic viewpoint and considered as an aggregate of crystals. The model regards the crystals as rigid‐viscoplastic and incorporates the effects of atomic debonding on the overall mechanical behavior. To illustrate the capability of the proposed model, two simulations are carried out to capture the macroscopic stress–strain behavior and texture evolution under uniaxial tension and simple shear loading conditions. The results are compared with experimental data and numerical simulations from other references. POLYM. ENG. SCI., 47:410–420, 2007. © 2007 Society of Plastics Engineers.

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)0.001
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 score0.395
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.230
Teacher spread0.214 · 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

Citations13
Published2007
Admission routes1
Has abstractyes

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