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Dynamic Transformation during Plate and Strip Rolling

2016· article· en· W2551746216 on OpenAlexaff
John J. Jonas, Clodualdo Aranas, Samuel Filgueiras Rodrigues, In‐Ho Jung

Bibliographic record

VenueMaterials science forum · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceAusteniteIsothermal processMetallurgyFerrite (magnet)SofteningVolume fractionComposite materialThermodynamicsMicrostructure

Abstract

fetched live from OpenAlex

Torsion simulations were carried out of both plate (long interpass times) and strip (short interpass times) rolling. Both isothermal and continuous cooling conditions were employed. The dynamic transformation of austenite to ferrite was observed under all conditions and at all temperatures within the austenite phase field. About 8 to 10 volume percent ferrite was formed in a given pass, leading to about 50 - 70 % ferrite at the end of selected simulations. During the interpass intervals, some retransformation to austenite took place, the amount of which increased with holding time and temperature and decreased with the addition of alloying elements. It is shown that the driving force for the transformation is the softening associated with the replacement of work-hardened austenite grains by the softer alpha phase. The implications with respect to rolling load (i.e. mean flow stress) are also discussed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.013

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 studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.004
GPT teacher head0.183
Teacher spread0.179 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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