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Mechanism for Loss of Hot Ductility Due to Deformation during Solidification in Continuous Casting of Steel

2004· article· en· W1997592412 on OpenAlexafffund
Faramarz Zarandi, Steve Yue

Bibliographic record

VenueISIJ International · 2004
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceDuctility (Earth science)MetallurgyContinuous castingCrackingDeformation (meteorology)MicrostructureMetallographyCastingScrapMicroalloyed steelComposite materialCreepAustenite

Abstract

fetched live from OpenAlex

Strand surface cracking is a problem that can lead to scrap the product of the continuous casting process. Stress and strain developed during continuous casting have been found to be effective in this respect. The crack susceptibility is usually evaluated by assessing the steel hot ductility at the straightening stage of the process. The hot ductility of two Nb-microalloyed steels was evaluated in in situ melted specimens. The billet surface thermal history was simulated in order to generate the billet surface microstructure. The effect of deformation incorporated with steel solidification was studied and it was found that any kind of deformation during solidification results in loss of hot ductility. Metallography of specimens revealed that deformation caused grain boundary cracking and changed the segregation pattern, leading to loss of hot ductility. Based on this, a mechanism for hot ductility loss was proposed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2004
Admission routes2
Has abstractyes

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