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Microstructure Model for a Dual-Phase Steel

2007· article· en· W2038567970 on OpenAlexafffund
Dongsheng Liu, Matthias Militzer, Warren J. Poole

Bibliographic record

VenueMaterials science forum · 2007
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceAusteniteMicrostructureRecrystallization (geology)Dynamic recrystallizationFerrite (magnet)MetallurgyContinuous cooling transformationDual-phase steelThermodynamicsComposite materialHot workingBainiteMartensite

Abstract

fetched live from OpenAlex

The microstructural evolution has been studied for hot rolling of a dual-phase steel with a lean C-Mn-Si chemistry. This study includes the investigation of austenite grain growth during reheating, constitutive behaviour and static recrystallization kinetics of austenite, and austenite decomposition during simulated run-out table cooling conditions. To develop and validate the microstructure models for these phenomena, experimental studies have been carried out in the laboratory using a Gleeble 3500 thermomechanical simulator. The hyperbolic sine relationship between flow stress and Zener-Hollomon parameter is employed to describe the constitutive behaviour. The Johnson-Mehl-Avrami-Kolmogorov (JMAK) theory is used to predict the static recrystallization kinetics. Ferrite transformation start is described with an approach that considers early growth of corner nucleated ferrite. The fraction of ferrite transformed from austenite during continuous cooling is described using the JMAK approach in combination with the additivity rule. The ferrite grain size is quantified as a function of the transformation start temperature. The overall microstructure model has been validated based on a number of laboratory simulations of the entire hot strip rolling and controlled cooling process with an emphasis on industrially relevant run-out table cooling strategies.

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 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 score0.068
Threshold uncertainty score0.574

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 science0.0000.000
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 head0.014
GPT teacher head0.258
Teacher spread0.244 · 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

Citations6
Published2007
Admission routes2
Has abstractyes

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