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Record W2069867434 · doi:10.1109/tmag.2014.2381160

Design and Optimization of a Voice Coil Actuator for Precision Motion Applications

2015· article· en· W2069867434 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

VenueIEEE Transactions on Magnetics · 2015
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectromagnetic coilInductanceActuatorComputer scienceFinite element methodAccelerationVoice coilMagnetic fieldMechanical engineeringControl theory (sociology)VoltageMaterials sciencePhysicsElectrical engineeringClassical mechanicsEngineering

Abstract

fetched live from OpenAlex

There has been increasing attention to the use of voice coil actuators (VCAs) for precision motion applications. In this paper, a detailed design of a cylindrical VCA is presented. Different options for the overall configuration are evaluated according to various criteria and design variables are defined for the chosen configuration. After that, optimization parameters are derived to maximize the performance in a precision motion application by maximizing the acceleration and minimizing the heat dissipation. Design parameters are optimized using finite element analysis to evaluate the magnetic properties. Optimization is carried out using the bulk volume of coil, which allowed electrical properties to be later characterized via the selection of wire gauge. Calculations for evaluating resistance, inductance, current drain, and voltage supply requirements for dc and dynamic cases are presented. Magnetic field predictions and formulations used in force calculations are verified in experiments.

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: Methods · Consensus signal: none
Teacher disagreement score0.845
Threshold uncertainty score0.425

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 head0.021
GPT teacher head0.234
Teacher spread0.213 · 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