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Effect of Multipass Severe Rolling Process in the API X65 Steel

2007· article· en· W1973433611 on OpenAlexaff
Ho Sup Sim, Ki Seop Cho, Kon Bae Lee, Hyung Ryul Yang, Hoon Kwon

Bibliographic record

VenueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena · 2007
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsLa Cité Collégiale
Fundersnot available
KeywordsMaterials sciencePearliteFerrite (magnet)Ultimate tensile strengthMicrostructureMartensiteMetallurgyToughnessCrackingSlow coolingYield (engineering)Dynamic strain agingContinuous cooling transformationComposite materialBainiteAustenite

Abstract

fetched live from OpenAlex

In the API X65 steel, effects of rolling and cooling conditions on microstructure and mechanical properties were studied. In the case of accelerated cooling after multi-pass rolling in the high/low unrecrystallized range, the tensile strength was 574-670 MPa and the impact toughness was 74-109 J. In the case of accelerated cooled to 550°C and then interrupted by air cooling, on the other hand, those values were 524-538 MPa and 100-135 J, respectively. Whereas the former exhibited the continuous yielding, the latter showed discontinuous yielding. In addition, yield ratio increased from 0.59-0.67 to 0.85-0.87, accompanied with the enhancement of yield strength. Ultrafine ferrite grains formed by the strain induced dynamic transformation during the severe rolling and second phases formed during cooling were observed. In accelerated cooling and interrupted cooling conditions, main second phases formed after cooling were martensite and pearlite, respectively. Separation cracking mostly observed at interfaces of ferrite matrix and second phases, may be attributed to the intrinsic interfacial weakness.

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)0.001
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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 head0.021
GPT teacher head0.289
Teacher spread0.268 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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Same venueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomenaSame topicMicrostructure and Mechanical Properties of SteelsFrench-language works237,207