MétaCan
Menu
Back to cohort
Record W1973783189 · doi:10.1007/s10921-014-0260-x

Correlation Between AC Core Loss and Surface Magnetic Barkhausen Noise in Electric Motor Steel

2014· article· en· W1973783189 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Nondestructive Evaluation · 2014
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsUniversity of AlbertaRoyal Military College of CanadaMcGill UniversityQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBarkhausen effectMaterials scienceElectrical steelEddy currentMagnetizationGrain sizeBarkhausen stability criterionNoise (video)Texture (cosmology)Nuclear magnetic resonanceAcousticsCondensed matter physicsComposite materialMagnetic fieldElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Core loss is a significant source of energy loss in electric motor steel laminates. Therefore, there is interest in monitoring the quality and consistency of laminates at various stages of manufacturing. The purpose of this study was to investigate the feasibility of using surface magnetic Barkhausen noise for the evaluation of AC core loss, and further, to examine potential origins of magnetic loss in non-oriented electrical steel. Core loss values were measured by a single sheet tester and Barkhausen noise measurements were performed using pole flux control on eight laminates with various grain size, texture and composition. Magnetocrystalline energy was calculated from X-ray diffraction data to quantify texture. Results demonstrated higher surface Barkhausen emissions for samples with lower core loss. Barkhausen noise analyses were used to examine the interplay among core loss, grain size, magnetocrystalline energy and B–H characteristics. The inverse correlation between core loss and Barkhausen noise emissions was qualitatively explained in terms of the orthogonal vector contribution of microscopic eddy currents to losses associated with bulk currents arising in the sample during magnetization.

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.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.029
GPT teacher head0.285
Teacher spread0.257 · 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