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Record W2620304539 · doi:10.5539/mer.v7n1p7

Motor Using Mechanical Vibration of Multiple Bimorph Beams

2017· article· en· W2620304539 on OpenAlexvenueno aff
Hiroyuki Yaguchi

Bibliographic record

VenueMechanical Engineering Research · 2017
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsnot available
Fundersnot available
KeywordsBimorphRotational speedTorqueRotor (electric)VoltageVibrationMaterials scienceCantileverAcousticsPhysicsMechanical engineeringEngineeringElectrical engineeringComposite material

Abstract

fetched live from OpenAlex

The present paper proposes a non-magnetic motor with a rotor rotated by the mechanical resonance energy of four bimorph cantilever beams excited by an electrostatic force. The use of a flexible material such as silicon rubber enables conversion of translational vibration to rotary movement in one direction. The rotational speed of the proposed motor increases in proportion to the input voltage when two bimorph beams are used, and the maximum rotational speed was found to be 6,804 rpm when the input voltage was set to 24.6 V. Next, the basic characteristics of a prototype motor with four bimorph cantilever beams, including rotational speed, output torque, and efficiency, were determined experimentally. The experimental results revealed that a maximum rotational speed of 6,370 rpm was obtained when the output torque was 19.6 uNm. The proposed motor was also observed to produce an output torque of 63.7 uNm when the rotational speed was 1,491 rpm. The maximum efficiency was 6.2% when the input power was 0.3 W. For the proposed motor, the volume and weight were reduced by approximately 35%, as compared with a motor from a previous study.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.296
Teacher spread0.253 · 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 designBench or experimental
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
Published2017
Admission routes1
Has abstractyes

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