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Effect of High Temperature Deformation on the Hot Ductility of Nb Microalloyed Steel

2005· article· en· W2089038006 on OpenAlexafffund
Faramarz Zarandi, Steve Yue

Bibliographic record

VenueMaterials science forum · 2005
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceContinuous castingDuctility (Earth science)MetallurgyDeformation (meteorology)Microalloyed steelHot workFerrite (magnet)MicrostructureThermalComposite materialAusteniteTool steelCreep

Abstract

fetched live from OpenAlex

Low hot ductility of steel at the straightening stage of the continuous casting process is a problem found in steels containing microalloying and/or certain alloying additions. The thermal schedule undergone by the billet surface in the mill has a significant effect on the hot ductility. In this work, thermomechanical processing was employed to alleviate the problem of hot ductility in the Nb-microalloyed steel. Specimens were melted in situ and subjected to the billet surface thermal schedule in order to generate a microstructure similar to that present at the straightening stage of the continuous casting process. Some deformation schedules were incorporated with the thermal schedule at very high temperatures, specifically during solidification, within the d-ferrite region, and during the d®g transformation, and the hot ductility was subsequently evaluated at the end of the thermal schedule where the straightening operation is performed. After the thermal schedule alone, the steel exhibited a very low hot ductility at the straightening stage. It was found that deformation at very high temperatures prior to the straightening stage had a considerable effect on the hot ductility, either detrimental or beneficial, depending on the region in which the deformation has been executed. The mechanisms leading to loss and improvement of hot ductility are explained in this paper.

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 score0.002
Threshold uncertainty score0.245

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 head0.004
GPT teacher head0.195
Teacher spread0.191 · 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

Citations4
Published2005
Admission routes2
Has abstractyes

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