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Flow Softening-based Formation of Widmanstätten Ferrite in a 0.06%C Steel Deformed Above the Ae<sub>3</sub>

2015· article· en· W2075385721 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueISIJ International · 2015
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsSofteningFerrite (magnet)Materials scienceFlow stressDynamic recrystallizationScanning electron microscopeStrain rateAtmospheric temperature rangeRecrystallization (geology)MetallurgyComposite materialAnalytical Chemistry (journal)CrystallographyThermodynamicsMicrostructureChemistryPhysicsGeologyHot working

Abstract

fetched live from OpenAlex

Compression tests were carried out at a strain rate of 1 s–1 on a 0.06%C-0.3%Mn-0.01%Si steel over two temperature ranges: i) 920°C to 980°C, and ii) 500 to 750°C. Optical and scanning electron microscopy images indicated that significant volume fractions of Widmanstätten ferrite were formed dynamically above the Ae3 temperature. The ferrite plates coalesced into polygonal grains during straining. The double differentiation method was applied to the stress-strain curves, providing average values for the dynamic transformation (DT) and dynamic recrystallization (DRX) critical strains of 0.12 and 0.20, respectively. These results are interpreted in terms of the flow softening-based transformation model by calculating both the driving forces promoting the transformation as well as the energy barriers that oppose it. The model predicts the temperature range over which DT can occur as well as the observed critical strains.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.585

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 head0.015
GPT teacher head0.209
Teacher spread0.195 · 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