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Record W2415120625 · doi:10.1109/icra.2016.7487474

Design and development of a hybrid Magneto-Rheological clutch for safe robotic applications

2016· article· en· W2415120625 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsWestern University
Fundersnot available
KeywordsClutchElectromagnetic coilMagnetFinite element methodTorqueMechanical engineeringComputer scienceMagnetic fieldPower (physics)MagnetoField (mathematics)Automotive engineeringElectrical engineeringControl engineeringEngineeringPhysicsStructural engineering

Abstract

fetched live from OpenAlex

In this paper, we present a new concept for generating magnetic field in Magneto-Rheological (MR) clutches intended for safe robotic applications. The main rationale behind this concept is to divide the magnetic field generation into two parts using an electromagnetic coil and a permanent magnet. The permanent magnet generates a bias magnetic field density at the optimum working point of the MR clutch while the energized coil can add or negate the magnetic field to a desired value. The results will show clear advantages of this concept in reducing the total weight of the MR clutch, improving the torque-to-mass ratio, and reducing electrical power consumption. The proposed concept is validated using computer model of an MR clutch and Finite Element Method (FEM) is hired to compare the characteristics of the proposed MR clutch with those from conventional coil based clutch. Experimental results will be provided to further validate the advantages of the proposed new concept.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.702
Threshold uncertainty score0.246

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 head0.023
GPT teacher head0.219
Teacher spread0.196 · 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

Quick stats

Citations25
Published2016
Admission routes1
Has abstractyes

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