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Record W2341371305 · doi:10.1109/tmag.2015.2512920

Effect of Shear Cutting on Microstructure and Magnetic Properties of Non-Oriented Electrical Steel

2015· article· en· W2341371305 on OpenAlexaff
Aroba Saleem, Natheer Alatawneh, Richard R. Chromik, David A. Lowther

Bibliographic record

VenueIEEE Transactions on Magnetics · 2015
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceMicrostructureElectrical steelNanoindentationComposite materialLaminationLaser cuttingPunchingScanning electron microscopeGrain sizeResidual stressEnhanced Data Rates for GSM EvolutionLaserOptics

Abstract

fetched live from OpenAlex

In manufacturing electrical machine cores, the electrical steel laminations are often mechanically cut, leading to residual stress and a deterioration in magnetic properties. Several cutting techniques are used in the industry, such as shear cutting, punching, and laser cutting. The influence of shear cutting on the steel microstructure and magnetic properties was investigated in this paper. A single sheet tester was used for the measurements of two different grades of non-oriented electrical steel at different induction levels (0.1-1.5 T) and a wide range frequency (3 Hz-1 kHz). A scanning electron microscope was used for the characterization of the microstructure (grain size) at the cutting edges. The mechanical properties near the edge of the lamination were measured using nanoindentation. An increase in magnetic loss due to cutting was observed to be ~20% for B35AV1900 and ~9% for 35WW300, corresponding to a damaged area extending up to a distance of ~170 and ~140 μm, from the cut edge, respectively.

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 score0.083
Threshold uncertainty score0.549

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 head0.009
GPT teacher head0.199
Teacher spread0.190 · 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

Citations30
Published2015
Admission routes1
Has abstractyes

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