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Record W4393454135 · doi:10.1016/j.jmrt.2024.03.241

Austenite formation in a medium-Mn steel during intercritical annealing via in situ high-energy X-ray diffraction

2024· article· en· W4393454135 on OpenAlexafffund
Azin Mehrabi, Hatem S. Zurob, Imed-Eddine Benrabah, Joseph R. McDermid

Bibliographic record

VenueJournal of Materials Research and Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster UniversityInternational Zinc AssociationCanadian Light Source
KeywordsAusteniteMaterials scienceIsothermal processMetallurgyAnnealing (glass)Ferrite (magnet)MicrostructureMartensiteDiffractionBainiteComposite materialThermodynamics

Abstract

fetched live from OpenAlex

The microstructural evolution of a prototype Fe-0.15C-5.56Mn-1.1Si-1.89Al medium-Mn third-generation advanced high strength steel (3G AHSS) with a martensite-ferrite (MF) starting microstructure during intercritical annealing was determined in situ using high energy X-ray diffraction (HEXRD). Intercritical annealing was carried out at 665 °C and 710 °C for 120 s and 240 s, followed by cooling to room temperature. HEXRD data were analyzed to monitor the austenite fraction and lattice parameters throughout the thermal cycle. During isothermal holding, the austenite fraction increased, up to 31% and 45% for the samples annealed for 120 s, and up to 33% and 46% for the samples annealed for 240 s at 665 °C and 710 °C, respectively. Observed changes in lattice parameters during isothermal holding were attributed to composition changes due to the partitioning of C between austenite and ferrite. Diffusion simulations using DICTRA were used to calculate solute partitioning during intercritical annealing, providing further insights into both austenite growth and the distribution of alloying elements within the austenite.

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 score0.010
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.268
Teacher spread0.253 · 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

Citations19
Published2024
Admission routes2
Has abstractyes

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