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Record W3181027322

Finite Element Modeling to Determine the Strain Rate Variation for Non-Viscous Deformation of HDPE During the Necking Process

2014· article· en· W3181027322 on OpenAlexaff
P.‐Y. Ben Jar, S. Muhammad

Bibliographic record

VenueSSRN Electronic Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicTransport Systems and Technology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNeckingDeformation (meteorology)Materials scienceStrain rateCreepStress (linguistics)High-density polyethyleneCrossheadStrain (injury)Composite materialFinite element methodStress–strain curveStructural engineeringPolyethylene
DOInot available

Abstract

fetched live from OpenAlex

Deformation for High Density Poly Ethylene (HDPE) and its stress response are measured during the necking process at the crosshead speed of 5 and 100 mm/min. Since necking in HDPE is known to involve a significant amount of creep deformation, the measured strain is generated from non-viscous deformation and creep deformation. It is the former that induces the stress response, not the latter. Therefore, it is important to determine the strain rate for the former to quantify the effect of the strain rate on the stress response. In this study, finite element modeling is used to extract non-viscous strain from the total strain during the necking process of HDPE. The results suggest that the range of strain rate for the non-viscous deformation can be reasonably represented by the strain rate measured experimentally based on the total strain, even though a significant amount of creep deformation occurs during the necking process.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.204
Teacher spread0.198 · 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 designSimulation or modeling
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

Citations0
Published2014
Admission routes1
Has abstractyes

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