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Record W2072952068 · doi:10.1142/s175882511250024x

ON THE LARGE STRAIN TORSION OF HCP POLYCRYSTALS

2012· article· en· W2072952068 on OpenAlexafffund
Pengfei Wu, Huamiao Wang, K.W. Neale

Bibliographic record

VenueInternational Journal of Applied Mechanics · 2012
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversité de SherbrookeMcMaster University
FundersOntario Ministry of Research and Innovation
KeywordsTorsion (gastropod)Materials scienceCrystal twinningCrystalliteFinite element methodCrystallographyComposite materialCondensed matter physicsMetallurgyThermodynamicsMicrostructurePhysicsChemistry

Abstract

fetched live from OpenAlex

The large strain torsion of polycrystalline materials with the hexagonal close packed (HCP) crystallographic structure is numerically studied by using a special purpose finite element. All simulations are based on the recently developed large strain elastic visco-plastic self-consistent (EVPSC) model for polycrystalline materials. For the first time, the effect of twinning on the large strain torsion is assessed in the present study. It is found that the response of the large strain torsion of HCP polycrystals is very sensitive to the initial texture and texture evolution. Numerical results indicate that excluding texture evolution dramatically reduces the development of the second-order axial strain under free-end torsion, or the axial force under fixed-end torsion. It is also numerically demonstrated that twinning has a significant influence on the large strain torsion of HCP polycrystals. For the magnesium alloy AZ31 extruded bar, the predicted results are in good qualitative agreement with experimental observations.

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 categoriesInsufficient payload (model declined to judge)
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 score0.449
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.249
Teacher spread0.233 · 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

Citations45
Published2012
Admission routes2
Has abstractyes

Explore more

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