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Record W4409987973 · doi:10.1016/j.mtcomm.2025.112692

Hot deformation characteristics of non-oriented electrical steels with and without phase transformation during thermomechanical processing

2025· article· en· W4409987973 on OpenAlexafffund
Gyanaranjan Mishra, Youliang He, Clodualdo Aranas

Bibliographic record

VenueMaterials Today Communications · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsNatural Resources CanadaUniversity of New Brunswick
FundersOffice of Energy Research and DevelopmentNatural Sciences and Engineering Research Council of CanadaNatural Resources CanadaNew Brunswick Innovation FoundationCanada Foundation for Innovation
KeywordsMaterials scienceThermomechanical processingTransformation (genetics)Deformation (meteorology)Phase (matter)Composite materialMetallurgyMicrostructure

Abstract

fetched live from OpenAlex

Hot rolling plays a critical role in determining the final microstructure, texture, and magnetic properties of non-oriented electrical steels (NOES). Especially, NOES with and without phase transformation not only show different flow behaviors during hot rolling but also result in different final microstructures and textures. However, detailed investigations on such different behaviors of NOES have rarely been seen in the literature. In this study, the hot deformation characteristics of two non-oriented electrical steels containing (weight percentage) 1.3 % Si (with phase transformation) and 3.2 % Si (without phase transformation) were investigated by uniaxial compression in a temperature range of 850–1050 °C and a strain rate range of 0.01–1.0 s −1 , to evaluate the differences in deformation behavior and microstructure. The stress-strain curves were modeled using Zener-Hollomon constitutive equations and the material parameters were obtained. It is demonstrated that the difference in deformation behavior between the two steels is due to the presence/absence of austenite-ferrite phase transformations, and to the difference in activation energy and the amount of precipitate-forming elements, which influence the work hardening and 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.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.461

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 communication0.0000.000
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 head0.008
GPT teacher head0.242
Teacher spread0.234 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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