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Record W2094591271 · doi:10.1088/0965-0393/17/2/025007

A void coalescence model for combined tension and shear

2009· article· en· W2094591271 on OpenAlexaff
C. Butcher, Zengtao Chen

Bibliographic record

VenueModelling and Simulation in Materials Science and Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsMaterials scienceNeckingCoalescence (physics)Shear (geology)Void (composites)MechanicsStructural engineeringUltimate tensile strengthLimit loadComposite materialFinite element methodEngineeringPhysics

Abstract

fetched live from OpenAlex

The influence of shear loading on damage development in Gurson-based models has long been neglected resulting in inadequate fracture strain predictions at low triaxiality where shear effects become significant. The plastic limit-load fracture criterion used in advanced Gurson models neglects the influence of shear loading and overestimates the fracture strain and porosity at low triaxiality. In this paper, we extend the recently proposed shear damage model of Xue [1] to provide a stronger physical foundation by removing the simplifying assumptions. Then we directly modify the plastic limit-load fracture criterion by coupling with the extended shear damage model to account for shear weakening and failure of the intervoid ligament in void coalescence. We apply the modified plastic limit-load criterion to predict the necking of sheet tensile specimens and find very good agreement with the available experimental results.

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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.249
Teacher spread0.225 · 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

Citations22
Published2009
Admission routes1
Has abstractyes

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