MétaCan
Menu
Back to cohort

Influence of Severe Accumulative Rolling in a Low Carbon Microalloyed Steel

2005· article· en· W2090040640 on OpenAlexaff
Ho Sup Sim, Kon Bae Lee, Hyung Ryul Yang, Hoon Kwon

Bibliographic record

VenueMaterials science forum · 2005
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsLa Cité Collégiale
FundersKorea Science and Engineering Foundation
KeywordsMaterials scienceBainitePearliteMetallurgyMicrostructureFerrite (magnet)HardenabilityAusteniteMartensiteUltimate tensile strengthMicroalloyed steelStrain hardening exponentDeformation (meteorology)Composite material

Abstract

fetched live from OpenAlex

Effect of the severe deformation by multi-pass rolling on microstructure and tensile properties was analyzed in terms of rolling temperature, plate thickness, and cooling rate for a modified API X65 steel containing B. The plates, 80 and 50 mm thickness, were rolled six times by 20%/pass (total 75%) to 20 and 12 mm, at 1023 K of unrecrystallized γ region or 973 K of intercritical (α+γ) region, and then quenched in water or oil. All specimens except one oil-quenched condition showed relatively high UTS 700-830 MPa and the continuous yielding(YR~0.6), typical mode of the (ferrite + martensite (bainite)) dual phase microstructure. In contrast, one oil-quenched specimen with the 973 K-20 mm condition, exhibited the discontinuous yielding (YR~0.8), indicating that the microstructure basically consists of ferrite plus pearlite, as well as a relatively low UTS 660 MPa. The degree of deformation really occurring within materials, i.e., strain hardening seems to be enhanced with a decrease in deformation temperature. As the degree of deformation increases, the remaining austenite, not dynamically transformed to fine ferrite, becomes increasingly unstable. A lower hardenability of this remaining austenite thus would lead to a higher possibility to transform into the (ferrite + pearlite) structure of lower strength rather than the (ferrite + martensite (bainite)) of higher strength.

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)0.000
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 score0.191
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.007
GPT teacher head0.214
Teacher spread0.207 · 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

Citations3
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueMaterials science forumSame topicMicrostructure and Mechanical Properties of SteelsFrench-language works237,207