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Record W3183875536 · doi:10.1016/j.jmrt.2021.07.107

Mechanical properties and precipitation behavior of high strength hot-rolled ferritic steel containing Nb and V

2021· article· en· W3183875536 on OpenAlexafffund
Esther Hutten, Shenglong Liang, E.M. Bellhouse, Sujay Sarkar, Yaping Lü, Brian Langelier, Hatem S. Zurob

Bibliographic record

VenueJournal of Materials Research and Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsMcMaster UniversityArcelorMittal (Canada)
FundersNational Research Council Canada
KeywordsMaterials sciencePrecipitationMetallurgyHigh strength steelHot rolled

Abstract

fetched live from OpenAlex

The mechanical properties and precipitation behavior of hot-rolled microalloyed steels with varied Nb and V additions were investigated. The steels had a predominantly ferritic microstructure with a very good combination of strength ( σ U T S = 800–1000 MPa), ductility (total elongation = 16–19%) and hole expansion ratio (32–34%). The strength increased with increasing Nb and V content. The stretch-flangeability of the steels did not deteriorate with increasing strength. The favorable hole expansion behavior of the steels is attributed to the fine ferrite grain size (1.6–1.8 μm) and uniformity of the microstructure, as well as the fine precipitates of Nb and V carbonitrides in the matrix. Nb carbonitrides formed during hot rolling and contributed to the grain refinement through their interaction with recrystallization. The co-precipitation of Nb and V carbonitrides during coiling was confirmed and characterized by 3D atom probe tomography . The formation of these fine precipitates contributed to further increase in the strength of the V-containing steel allowing the steel to achieve strength >1000 MPa.

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 score0.003
Threshold uncertainty score0.005

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 studies0.0000.000
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 head0.040
GPT teacher head0.275
Teacher spread0.235 · 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

Citations31
Published2021
Admission routes2
Has abstractyes

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