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Record W2838250233 · doi:10.5539/jmsr.v7n3p78

Strain Aging in Boron Alloyed Multi-Phase С- Mn – Si – Steel Wire Rod

2018· article· en· W2838250233 on OpenAlexvenueno aff
A. I. Nesterenko, Alexander B. Sychkov, V. L. Plyuta, M. A. Blokhin

Bibliographic record

VenueJournal of Materials Science Research · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceBoronDynamic strain agingBainiteFerrite (magnet)MartensiteMetallurgyPearliteDuctility (Earth science)MicrostructureStrain (injury)DislocationComposite materialAusteniteStrain rate

Abstract

fetched live from OpenAlex

The specimens of C – Mn – Si - steel wire rod 5.5 mm in diameter with 0.005% B and without B additions from Moldova Steel Works had been studied. It was been established by crystallographic-geometrical analysis, that the boron atoms could be allocate in B – alloyed a – Fe only in the positions of a sub-interstitial solid solution. By study a static strain aging (SSA) and a dynamic strain aging (DSA) specimens of wire rod by C – Mn – Si - steel with B and without B (both with multi-phase (ferrite- martensite (bainite)-pearlite) microstructures) more expressed decreasing of strengthening properties and higher characteristics of ductility had been determined for C – Mn – Si - steel specimens with B. These results could be explained by “de - nitrogenous” and “de - carbonaceous” mechanisms, when boron atoms from a – Fe sub-interstitial solid solution by producing wire rod and by its strain aging thermal treatment with temperatures 150 - 450 °С generate boron- nitrogenous and boron - carbonaceous – nitrogenous precipitations. By realizing these mechanisms nitrogen and carbon atoms are partly excluded from the dislocation pinning’s process. This, in fact, explains to inhibit the development of strain aging (SSA and DSA) in boron- micro-alloyed C – Mn – Si - steel.

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)0.007
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 score0.006
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.075
GPT teacher head0.381
Teacher spread0.307 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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