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Ultra-Fine Grained High Carbon Steel by Innovative Deformation

2007· article· en· W2107711858 on OpenAlexaff
Arthur W. Smith, D. N. Crowther, Peter Apps, P.B. Prangnell

Bibliographic record

VenueMaterials science forum · 2007
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsLarus Technologies (Canada)
Fundersnot available
KeywordsMaterials scienceMicrostructureElectron backscatter diffractionEqual channel angular extrusionUltimate tensile strengthFerrite (magnet)CarbideMetallurgyEutectic systemSevere plastic deformationGrain sizeComposite material

Abstract

fetched live from OpenAlex

It is well known that the refinement of grain size in metals leads to a significant improvement in specific mechanical properties. Processing schedules have been investigated aimed at producing an homogeneous ultra-fine ferrite and spheroidised carbide aggregate microstructure in high carbon (CMn) steels (0.6-1.2wt%C), via conventional ‘warm’ rolling and innovative Equal Channel Angular Extrusion (ECAE). Suitable deformation schedules were determined from dilatometry and thermo-mechanical Gleeble simulations. Evidence of an ultra-fine ferrite and carbide aggregate microstructure following ‘warm’ rolling was observed. A significant improvement in tensile strength, particularly proof stress was also noted in comparison to material deformed at higher temperatures. Concurrent ECAE experiments investigated microstructural evolution with incremental strain. Extensive analysis was carried out using various techniques, including high resolution Electron Back Scattered Diffraction (EBSD). Evidence of ferrite grain refinement was noted in a eutectoid composition steel. A sub-micron ferrite structure was observed following high strains (ε~3.33) and the mechanical properties exhibited a marked increase in tensile 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)0.003
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 score0.004
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.008
GPT teacher head0.229
Teacher spread0.221 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

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