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MagFloor: a Universal Magnetic Levitation Platform for Flexible Manufacturing

2024· article· en· W4405846327 on OpenAlexafffund
Yang Wang, Mir Behrad Khamesee

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMagnetic Bearings and Levitation Dynamics
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMagnetic levitationLevitationComputer scienceMechanical engineeringEngineeringMagnet

Abstract

fetched live from OpenAlex

The magnetic levitation (maglev) technology supports contactless and frictionless operations using permanent-magnet movers and coil stators. The movers can be constructed using Halbach arrays (HAs) or disc magnets (DMs). The MagFloor platform equipped with a square coil array is developed to actuate both types of movers compatibly, covering a 1.8m-by-2.4m horizontal working area. The Lorentz force and torque (wrench) models between the magnet movers and copper-wired square coils are established using data-driven approaches. The implementation time cost of the wrench models for the HA and DM movers on the programmable logic controller (PLC) is 2.3 μs and 4.1 μs per mover-coil-pair, respectively. The mover poses are measured using an optical sensing system and controlled at 1 kHz. The HA mover has unlimited yaw angle rotations and a maximum 7.5mm air gap. The maximum air gap and roll and pitch range of the DM mover are 65mm and ±45°, respectively. The horizontal motion range for both movers is only limited by the platform geometry. The HA and DM movers verified on a small-scale platform will be studied on the proposed maglev platform (MLP). The horizontal operating range of the MLP is expandable using a modular design to a large scale for future research on flexible manufacturing applications.

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: Empirical · Consensus signal: none
Teacher disagreement score0.793
Threshold uncertainty score0.558

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)0.0010.000

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 head0.217
Teacher spread0.205 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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