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Design and Control of a Reluctance-Based Micropositioning Stage for Scanning Motion Applications

2023· article· en· W4389665414 on OpenAlexaff
Mohammad Al Saaideh, Natheer Alatawneh, Khaled F. Aljanaideh, Mohammad Al Janaideh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsUniversity of GuelphMemorial University of Newfoundland
Fundersnot available
KeywordsMagnetic reluctanceControl theory (sociology)Feed forwardActuatorController (irrigation)Displacement (psychology)Process (computing)Frequency domainEngineeringComputer scienceControl engineeringMechanical engineeringControl (management)Magnet

Abstract

fetched live from OpenAlex

This paper presents a design and characterization of a micropositioning stage driven by a reluctance actuator. The stage is constructed with a C-core reluctance actuator and four compression springs. The design of the stage is presented using a CAD model, followed by the fabrication process of the prototype. The mathematical model is formulated to present the interaction among the stage's electrical, magnetic, and mechanical dynamic behaviour. Next, the force-current and force-gap characteristics are obtained by measuring the force under different applied currents and air gaps. After that, the system is analyzed to determine the maximum applied voltage that stabilizes the system in an open-loop configuration, followed by the time-domain and frequency-domain response. Finally, the feedforward controller is presented to linearize the dynamic behavior of the stage over a specific range of motion. The experimental results under the feedforward controller show a linear characteristic between the desired force and the output displacement.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.238

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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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
GenreMethods

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

Citations3
Published2023
Admission routes1
Has abstractyes

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