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The Influence of Peak Temperature and Deformation on Welding CCT Diagram of Eutectoid Carbon Steel

2006· article· en· W2021153487 on OpenAlexaff
Mehran Maalekian, M.L. Lendinez, Ernst Kozeschnik, H.P. Brantner, H. Cerjak

Bibliographic record

VenueAdvanced materials research · 2006
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Effects on Materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEutectic systemMaterials scienceContinuous cooling transformationAustenitePearliteDiagramMetallurgyWeldingIsothermal transformation diagramDeformation (meteorology)MartensiteCarbon steelComposite materialMicrostructureBainiteMathematics

Abstract

fetched live from OpenAlex

The welding continuous cooling transformation (WCCT) behavior of eutectoid carbon steel was investigated in different peak temperatures and in the undeformed and deformed conditions. The corresponding WCCT and welding continuous cooling compression transformation (WCCCT) diagrams were constructed by means of dilatometric and metallographic analyses in addition to hardness measurements. It was found that the higher austenitizing temperature slightly accelerates pearlitic transformation, i.e., it shifts the WCCT diagram to shorter times. Furthermore, heavy hot deformation of austenite could strongly promote the formation of pearlite, that is, the WCCCT diagram moved toward the top left corner compared to the WCCT diagram, while martensite start temperature was lowered slightly, which is a characteristic of a displacive transformation mechanism.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.006
GPT teacher head0.265
Teacher spread0.259 · 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

Citations3
Published2006
Admission routes1
Has abstractyes

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