MétaCan
Menu
Back to cohort
Record W2799487121 · doi:10.1002/srin.201700547

Effect of Grain Size and Residual Strain on the Dynamic Transformation of Austenite under Plate Rolling Conditions

2018· article· en· W2799487121 on OpenAlexafffund
Samuel Filgueiras Rodrigues, Clodualdo Aranas, Binhan Sun, Fulvio Siciliano, Stephen Yue, John J. Jonas

Bibliographic record

Venuesteel research international · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsMaterials scienceAusteniteFerrite (magnet)Volume fractionMetallurgyGrain sizeMartensiteDiffusionless transformationResidual stressComposite materialMicrostructure

Abstract

fetched live from OpenAlex

When austenite is deformed within the austenite phase field, that is, above the equilibrium transformation temperature Ae 3 , it partially transforms dynamically into ferrite. Here, rough rolling simulations are conducted at 1100 °C on an X70 steel to study the influence of grain size and residual strain on this type of transformation. Three simulated roughing passes are applied with pass strains of 0.4 at a strain rate of 1.0 s −1 . The resulting stress–strain curves are recorded and analyzed. The volume fractions of ferrite and martensite (prior austenite) are determined on quenched samples by means of metallographic techniques. It is observed that dynamic transformation took place during the first roughing pass and that the volume fraction of transformed ferrite increases with the cumulative strain. Both the critical strain to the onset of dynamic transformation as well as the grain size decrease with pass number.

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 score0.063
Threshold uncertainty score0.243

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 head0.026
GPT teacher head0.332
Teacher spread0.306 · 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

Citations12
Published2018
Admission routes2
Has abstractyes

Explore more

Same venuesteel research internationalSame topicMicrostructure and Mechanical Properties of SteelsFrench-language works237,207