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Record W4281714880 · doi:10.1111/ffe.13750

Influence of Lode parameter on damage and fracture behavior of polyethylene materials

2022· article· en· W4281714880 on OpenAlexaff
Yi Zhang, Limei Han, Liang Qiao, Junming Fan, Bo Zhou

Bibliographic record

VenueFatigue & Fracture of Engineering Materials & Structures · 2022
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsUniversity of Alberta
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsLodeMaterials scienceFracture (geology)RADIUSDisplacement (psychology)Constitutive equationComposite materialFinite element methodSeries (stratigraphy)Stress (linguistics)Structural engineeringMetallurgyEngineeringGeology

Abstract

fetched live from OpenAlex

Abstract In this study, a series of tests on notched polyethylene (PE) plate specimens providing clues to damage and fracture behavior for a wide range of Lode parameters was carried out. Two series of numerical simulations of each test were performed, the first one without taking damage evolution into consideration and the second one taking damage evolution into consideration. Good correlation of experimental and numerical results in terms of the engineering stress–displacement relation has been achieved. The results show that the average Lode parameter increases with the increasing notch radius of the plate specimens. In addition, the damaged and undamaged constitutive equations were determined from the first and second series of finite element (FE) simulation, respectively. The critical damage parameter calculated from the damaged and undamaged constitutive equations was found to decrease when the average Lode parameter is increased. Furthermore, fracture occurs at the center of the minimum cross section, where the maximum damage parameter and plastic strain occur. The fracture strain was found to increase with the increase in the average Lode parameter.

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 categoriesMeta-epidemiology (narrow)
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 score0.381
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.008
GPT teacher head0.218
Teacher spread0.210 · 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.

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
Published2022
Admission routes1
Has abstractyes

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