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Record W2322912110 · doi:10.2514/6.2004-1890

Design and Development of Piezoelectric Inchworm Actuator

2004· article· en· W2322912110 on OpenAlexaff
Jian Li, Ramin Sedaghati, Javad Dargahi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsConcordia University
Fundersnot available
KeywordsActuatorPiezoelectricityComputer scienceMaterials scienceAcousticsPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

The objective of this research is to present a proof-of-concept design of an inchworm type piezoelectric actuator with output of maximum displacement and force (or power) for shape control and vibration control of adaptive truss structures. The proposed inchworm actuator adopts “pusher” type with frictional clamping mechanisms. It consists of three main components: two clamping or braking devices and one expanding device. The two frictional clamping devices provide alternating braking forces when the center shaft is moving inside the PZT tubal stack and emulates an inchworm, summing small steps to achieve large displacements. Since the development of a robust clamping mechanism is essential to realize the high force capability, a considerable design effort has been focused on optimizing the clamping device to increase the output force. CATIA is used as a platform to model the whole actuator and ANSYS is used to analyze and optimize the performance of the actuator. The complete design of the proposed actuator has been performed using the finite element analysis. The simulation result confirms that the output force of 74 Newton and incremental displacement in each step of 9 micron can be achieved using the proposed actuator. A prototype of actuator has been fabricated and static tests have been performed to validate the simulation results. The dynamic test of the actuator is currently under process and the result of the dynamic test will be subsequently compared with simulation results.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.783
Threshold uncertainty score0.161

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 head0.013
GPT teacher head0.192
Teacher spread0.180 · 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
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

Citations6
Published2004
Admission routes1
Has abstractyes

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