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Record W4361204477 · doi:10.1002/srin.202200952

Austenite Nucleation and Growth as a Function of Starting Microstructure for a Fe–0.15C–5.56Mn–1.1Si–1.89Al Medium‐Mn Steel

2023· article· en· W4361204477 on OpenAlexafffund
Azin Mehrabi, Joseph R. McDermid, Xiang Wang, Hatem S. Zurob

Bibliographic record

Venuesteel research international · 2023
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaInternational Zinc Association
KeywordsCementiteAusteniteMicrostructureMaterials scienceMetallurgyBainiteFerrite (magnet)NucleationMartensiteAnnealing (glass)Composite materialThermodynamics

Abstract

fetched live from OpenAlex

The effects of starting microstructure and intercritical annealing temperature on the phase‐transformation kinetics and microstructural evolution of a prototype Fe–0.15C–5.56Mn–1.1Si–1.89Al medium‐Mn third‐generation advanced high‐strength steel are examined. The starting microstructures comprise 1) an as‐received cold‐rolled (CR) microstructure containing a significant fraction of ferrite and tempered martensite and 2) an austenitized and quenched martensite–ferrite (MF) microstructure. Based on the microstructural observations, two different scenarios for austenite formation during intercritical annealing are proposed. For the CR starting microstructure, austenite can nucleate at ferrite/cementite interfaces, and at ferrite grain boundaries. In the case of the MF starting microstructure, which contains thin films of interlath retained austenite (RA), austenite forms on the martensite lath boundaries or grows directly from the existing interlath RA. The studies are interpreted using the DICTRA module of Thermo‐Calc. At 665 °C, the austenite reversion kinetics of the MF samples are faster than the transformation kinetics in the CR structure. At higher temperatures where most of the cementite has dissolved, the austenite fraction in both starting microstructures is very similar.

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 score0.459
Threshold uncertainty score0.628

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 communication0.0000.000
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 head0.041
GPT teacher head0.313
Teacher spread0.272 · 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

Citations11
Published2023
Admission routes2
Has abstractyes

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