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Record W2409645903 · doi:10.1088/0965-0393/23/8/085002

Factors contributing to plastic strain amplification in slip dominated deformation of magnesium alloys

2015· article· en· W2409645903 on OpenAlexafffund
Chad W. Sinclair, Guilhem Martin, Ricardo A. Lebensohn

Bibliographic record

VenueModelling and Simulation in Materials Science and Engineering · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceCrystal twinningSlip (aerodynamics)PlasticityAnisotropyCrystal plasticitySlip line fieldDeformation (meteorology)MagnesiumComposite materialMetallurgyDislocationThermodynamicsMicrostructureOpticsPhysics

Abstract

fetched live from OpenAlex

While plastic strains are never distributed uniformly in polycrystals, it has recently been shown experimentally that the distribution can be extremely heterogeneous in magnesium polycrystals even when the deformation is dominated by slip. Here, we attempt to provide insight into the (macroscopic) factors that contribute to this strain amplification and to explain, from a local perspective, the origins of this strain amplification. To do this, full field VPFFT crystal plasticity simulations have been performed under the simplifying assumption that twinning is inoperative. It is shown that the experimentally observed heterogeneity can be reproduced when a sufficiently high anisotropy in slip system strength is assumed. This can be further accentuated by a weakening of the texture.

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

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.042
GPT teacher head0.251
Teacher spread0.209 · 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

Citations1
Published2015
Admission routes2
Has abstractyes

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