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Texture Development during Grain Growth in Nonoriented Electrical Steels

2005· article· en· W2089704539 on OpenAlexaff
Jong‐Tae Park, Jerzy A. Szpunar

Bibliographic record

VenueISIJ International · 2005
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceGrain sizeTexture (cosmology)Grain growthElectrical steelMetallurgyGrain boundaryAbnormal grain growthCore (optical fiber)Composite materialMicrostructureArtificial intelligence

Abstract

fetched live from OpenAlex

Nonoriented electrical steels should have both low core loss and high permeability.These magnetic properties are largely affected by grain size and texture.The research on grain size optimization during grain growth has been very extensive whereas little attention has been paid to texture transformation during grain growth.In the present study, based on obtained experimental results, a mechanism of texture development during grain growth in nonoriented electrical steels is proposed.In the 2 % Si specimens, the major texture components, Goss and {111}͗112͘ components, are weakened during grain growth.In the 1 % Si specimens, the main texture components, {111}͗112͘ and {111}͗110͘, are strengthened.It is proposed that for a texture component to be continuously strengthened during grain growth, the grains of the specific orientation should have not only a size advantage over those of other orientations, but also a higher frequency of high angle, high energy grain boundaries.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.007
GPT teacher head0.227
Teacher spread0.221 · 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 designObservational
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

Citations47
Published2005
Admission routes1
Has abstractyes

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