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Record W3132114262 · doi:10.1016/j.jmrt.2021.02.075

Influence of the cooling rate below Ms on the martensitic transformation in a low alloy medium-carbon steel

2021· article· en· W3132114262 on OpenAlexafffund
J.H. Liu, N. Binot, Denis Delagnes, Mohammad Jahazi

Bibliographic record

VenueJournal of Materials Research and Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAusteniteMaterials scienceDiffusionless transformationMartensiteAlloyTransformation (genetics)MetallurgyDiffractionCarbon fibersThermodynamicsComposite materialMicrostructureOptics

Abstract

fetched live from OpenAlex

The influence of cooling rate below the martensite start temperature, Ms, on the kinetics of martensitic transformation in a medium carbon low alloy steel was determined using high resolution dilatometry, optical microscopy and X-ray diffraction techniques. A two-stage transformation was observed for slow cooling rates while at higher cooling rates, martensitic transformation occured through a single stage process. It is shown that the Koistinen–Marburger equation cannot adequately describe the observed two-stage transformation. A new equation is proposed in order to model the evolution of martensitic transformation by considering the influence of post Ms cooling rate. The method considers contributions from both, the initial austenite and the carbon enriched austenite. The underlying mechanisms are discussed and validated with experimental findings.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.243
Teacher spread0.227 · 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

Citations26
Published2021
Admission routes2
Has abstractyes

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