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Record W4281710278 · doi:10.1007/s11661-022-06717-y

Effect of Aluminum Content on the Dynamic Recrystallization of Fe18MnxAl0.74C Steels During Hot-Forging Treatments

2022· article· en· W4281710278 on OpenAlexafffund
J.S. Rodríguez, J. F. Durán, Y. Aguilar, G. A. Pérez Alcázar, Roberto Martins de Souza, O.A. Zambrano

Bibliographic record

VenueMetallurgical and Materials Transactions A · 2022
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsNational Research Council Canada
FundersNational Research Council CanadaUniversidad del ValleDepartamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)
KeywordsTwipMaterials scienceElectron backscatter diffractionPlasticityRecrystallization (geology)Crystal twinningForgingNucleationDynamic recrystallizationMetallurgyStacking-fault energyAlloyAluminiumHot workingMicrostructureComposite materialThermodynamics

Abstract

fetched live from OpenAlex

Abstract In the present work, the dynamic recrystallization and microstructural evolution of the family of advanced high-strength steels Fe18Mn x Al0.74C are studied, varying the aluminum content in 0, 3, 6, and 9 wt pct subjected to hot-forging treatments through three consecutive heating-deformation cycles. For characterization, X-ray diffraction (XRD), Mössbauer absorption spectroscopy (MAS), and electron backscattering diffraction (EBSD) were used. It was determined that for the steels under study, dynamic recrystallization occurs due to strain-induced boundary migration (SIBM) and is strongly influenced by the aluminum content of the alloy and its stacking failure energy (SFE), increasing that the aluminum content will generate greater nucleation sites, favoring the refinement of grains in the material and achieving a crystalline structure of random crystallographic orientation. The results are discussed throughout the article, allowing us to determine potential processing routes for advanced high-strength steels with predominantly plastic deformation mechanisms such as transformation-induced plasticity (TRIP), twinning-induced plasticity (TWIP), and microband-induced plasticity (MBIP).

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.011
GPT teacher head0.193
Teacher spread0.183 · 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.

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

Citations20
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueMetallurgical and Materials Transactions ASame topicMicrostructure and Mechanical Properties of SteelsFrench-language works237,207