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Record W3083091976 · doi:10.1049/iet-epa.2020.0218

Modelling and analysis of the effects of cutting of core laminations in electric machines

2020· article· en· W3083091976 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

VenueIET Electric Power Applications · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Machining and Optimization Techniques
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCore (optical fiber)Mechanical engineeringEngineeringComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Experimental measurements of electrical machine performances reveal a significant difference between simulations and measurements. It is known that the manufacturing processes of machines, particularly, the cutting of core laminations adversely affects material properties. The current techniques for considering the effects of cutting lack accuracy, computation efficiency and applicability for finite element (FE) software. This study proposes a method to model the variation in the magnetic properties of electrical steel due to cutting based on a non‐equal multi‐slice technique. In this work, the method has been validated and implemented on a FE permanent magnet synchronous motor model. Also, the proposed method has been compared with the single‐layer method.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.841
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
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 head0.006
GPT teacher head0.228
Teacher spread0.222 · 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