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Ferrite Formation above the Ae<sub>3</sub> Temperature during the Torsion Simulation of Strip Rolling

2015· article· en· W2304293691 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
TopicMetallurgy and Material Forming
Canadian institutionsMcGill University
FundersMcGill University
KeywordsMaterials scienceIsothermal processSofteningVolume fractionTorsion (gastropod)Ferrite (magnet)Dynamic recrystallizationMicroalloyed steelRecrystallization (geology)MetallurgyFlow stressMicrostructureComposite materialAusteniteThermodynamicsHot workingPhysics

Abstract

fetched live from OpenAlex

Torsion simulations of 7-pass strip rolling were carried out on a 0.06%C-0.3%Mn-0.01%Si and a 0.11%C-1.0%Mn-0.11%Si-0.03%Al-0.034%Nb steel using pass strains of 0.4 applied at 1 s−1. The deformations were imposed isothermally at 910°C and 930°C for the C–Mn and the Nb microalloyed steel, respectively. The flow curve levels decreased from pass to pass as a result of softening by both dynamic transformation (DT) and dynamic recrystallization (DRX). The application of double differentiation to the stress-strain curves led to average critical strains for the initiation of DT and DRX of about 0.06 and 0.11, respectively. Optical microscopy revealed that the volume fraction of DT ferrite increased continuously right up to the last pass. The fraction of DT ferrite formed and retained was significantly higher when short interpass times were used. Comparison of the behaviors of the C–Mn and Nb steels indicates that Nb addition retards both the forward as well as the reverse transformation.

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 score0.022
Threshold uncertainty score0.381

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 head0.012
GPT teacher head0.223
Teacher spread0.211 · 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