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Modeling Reluctance Actuator Topologies with a Focus on Stiffness

2024· article· en· W4402264814 on OpenAlexaff
Michael Pumphrey, Mohammad Al Saaideh, Natheer Alatawneh, Mohammad Al Janaideh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMemorial University of NewfoundlandUniversity of Guelph
Fundersnot available
KeywordsActuatorMagnetic reluctanceNetwork topologyStiffnessFocus (optics)Computer scienceControl theory (sociology)Structural engineeringControl engineeringEngineeringMechanical engineeringPhysicsMagnetControl (management)Artificial intelligenceComputer network

Abstract

fetched live from OpenAlex

The reluctance actuator (RA) can provide more acceleration than the Lorentz actuators that are currently in use in next-generation wafer scanners used in semiconductor lithography systems. These wafer scanners are utilized in semiconductor lithography systems. Due to the significant amount of nonlinearity that exists between the RA's input current and output force, it might be challenging to build an effective control system. In the ten years prior, a great number of studies contributed to the modelling, design, and control of RAs; however, the geometrical topology of the actuator was not evaluated from the standpoint of control. This study presents topology selection criteria with the intention of improving the control design process. Additionally, it assesses the various accessible RA topologies, including C and E cores, with reference to the modelling of stiffness. The C-core RA was shown to have a lower magnitude of stiffness at low air gap values (less than 0.5 mm) when compared to the E-core RA. This was discovered through simulation as well as through experimentation.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.633
Threshold uncertainty score0.274

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 head0.012
GPT teacher head0.216
Teacher spread0.204 · 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 designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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