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Simulation of Austenite Flow Curves under Industrial Rolling Conditions Using a Physical Dynamic Recrystallization Model

2012· article· en· W2089137165 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 · 2012
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsMcGill University
FundersArcelorMittalMcGill University
KeywordsDynamic recrystallizationFlow stressMaterials scienceMetallurgySofteningAusteniteWork hardeningRecrystallization (geology)Strain rateHot workingStrain hardening exponentRolling millHardening (computing)Composite materialMicrostructureGeologyMechanical engineering

Abstract

fetched live from OpenAlex

Hot compression tests were carried out on three steels: i) a 0.038% Nb-0.11%C microalloyed grade; ii) a Nb-modified TRIP steel; and iii) a Ti-stabilized low carbon steel. The tests were performed at strain rates up to 1 s–1 and over the temperature range 880–1200°C. The initiation of dynamic recrystallization (DRX) was observed under all testing conditions. Two sets of equations were derived from the experimental curves: i) a work hardening relation pertaining to the grains in which DRX has not yet nucleated; and ii) a separate work hardening expression describing the mean flow stress applicable to the grains in which DRX is taking place. With the aid of the temperature and strain rate dependences determined from the data, and using the law of mixtures, extrapolated flow curves were calculated applicable to strain rates up to 100 s–1, i.e. to those involved in strip mill rolling. The simulations show that, once DRX has been initiated, the flow stress is controlled by the kinetics of the softening mechanisms.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score0.387

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 communication0.0000.001
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 head0.066
GPT teacher head0.319
Teacher spread0.253 · 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