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Record W2011721247 · doi:10.3139/146.111062

Classical controlled rolling of low C steels microalloyed with Ti and Mo

2014· article· en· W2011721247 on OpenAlexafffund
Jessica Calvo, Laurie Collins, Stephen Yue

Bibliographic record

VenueInternational Journal of Materials Research (formerly Zeitschrift fuer Metallkunde) · 2014
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsMcGill UniversityEVRAZ (Canada)
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceAusteniteMicroalloyed steelMetallurgyTorsion (gastropod)Deformation (meteorology)Composite materialMicrostructure

Abstract

fetched live from OpenAlex

Abstract Two low-C steels, one microalloyed with Ti and the other with Ti and Mo were subjected to a classical controlled rolling schedule using torsion testing. The specific torsion schedules were a simulation of an industrial schedule adapted to the characteristic transformation temperatures of each steel. It is shown that such steels can develop a pancaked austenite, despite the fact that the alloys do not contain Nb. However, compared to Nb bearing steels, restoration between deformation passes is relatively rapid in the pancaking region. In order to reduce the loss of pancaking, the temperatures of the finishing rolling schedule were shifted to lower temperatures. This appeared to greatly reduce the restoration rates, but the transformation in the two phase region occurred towards the end of the final deformation pass. When comparing the two steels, it could be detected that Mo increases the temperature range for pancaking and reduces the restoration rates between deformation passes in the pancaking region.

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 score0.015
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.272
Teacher spread0.255 · 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

Citations14
Published2014
Admission routes2
Has abstractyes

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