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A Method to Incorporate Grain Boundary Strength and its Effects on Plastic Deformation in FCC Polycrystals

2020· article· en· W3108919515 on OpenAlexaff
Waqas Muhammad, Abhijit Brahme, Jidong Kang, Edward Cyr, D.S. Wilkinson, Kaan Inal

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2020
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsMcMaster UniversityUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceElectron backscatter diffractionGrain boundaryFinite element methodTexture (cosmology)Digital image correlationGrain boundary strengtheningDeformation (meteorology)Tension (geology)Composite materialPlasticityMetallurgyGeometryMicrostructureUltimate tensile strengthStructural engineeringMathematicsImage (mathematics)

Abstract

fetched live from OpenAlex

Abstract A three-dimensional (3D) crystal plasticity finite element method (CPFEM) is developed to investigate the effect of grain boundary strength on heterogeneous strain partitioning in FCC polycrystals. The proposed method incorporates electron backscatter diffraction (EBSD) maps into finite element analyses. The numerical analysis accounts for crystallographic texture, its evolution and 3D grain morphologies. Furthermore, grain boundaries are also mapped with a special finite element framework that allows material properties to be assigned to the grain boundaries. The material parameters and the grain boundary strengths are obtained by calibration to experimental uniaxial tension curves for single and polycrystals. Numerical simulations of uniaxial tension are performed and the effects of grain boundary strength on the onset of non-uniform deformation is investigated. The predicted local strain evolution is compared with corresponding experimental results from digital image correlation (DIC) measurements of an AA5754 aluminium sheet. The results showed that an excellent agreement was reached when the grain boundary properties were set so that the hardness was five times that of the average polycrystal response.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.003
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.224
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2020
Admission routes1
Has abstractyes

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