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Record W2049023667 · doi:10.3139/146.110307

Phase field modelling of austenite formation from ultrafine ferrite–carbide aggregates in Fe–C

2010· article· en· W2049023667 on OpenAlexaff
Hamid Azizi-Alizamini, Matthias Militzer

Bibliographic record

VenueInternational Journal of Materials Research (formerly Zeitschrift fuer Metallkunde) · 2010
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCementiteMaterials scienceAusteniteNucleationPhase field modelsFerrite (magnet)CarbideIsothermal processMetallurgyPhase (matter)MicrostructureComposite materialThermodynamics

Abstract

fetched live from OpenAlex

Abstract In this paper, austenite formation in an Fe – C system was simulated with a phase field approach. The model deals with a detailed description of morphological changes during austenite formation from ultrafine ferrite – cementite aggregates isothermally annealed in the intercritical region to form dual phase microstructures. Long-range diffusion of carbon is explicitly considered. The model is capable of resolving carbide particle sizes of about 100 nm to simulate the morphological complexity during austenite formation. Simulations were carried out in two- and three-dimensions. It was observed that morphological aspects of austenite formation depend significantly on spacing and distribution of cementite particles that provide suitable nucleation sites for austenite. This dependency can primarily be attributed to overlapping diffusion fields and curvature effects.

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)0.000
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: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0010.000
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 head0.049
GPT teacher head0.327
Teacher spread0.278 · 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

Citations19
Published2010
Admission routes1
Has abstractyes

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