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Record W4293056267 · doi:10.1109/aim52237.2022.9863307

A Monolithic Flexible Transmission for Piezoelectric Actuators

2022· article· en· W4293056267 on OpenAlexafffund
Ariel Tabak, Ryan Orszulik

Bibliographic record

Venue2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) · 2022
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFabricationTransmission (telecommunications)ActuatorFinite element methodDisplacement (psychology)Mechanism (biology)Compliant mechanismMaterials scienceComputer scienceElectronic engineeringEngineeringElectrical engineeringStructural engineeringTelecommunications

Abstract

fetched live from OpenAlex

The design, fabrication, and assembly of mesoscale robots can require expensive manufacturing equipment with multiple fabrication and assembly steps. This work presents the design of a monolithic transmission through the use of compliant mechanisms. The transmission mechanism is modelled using finite element analysis and is used to determine critical parameters for the conversion of small linear displacement to large rotational motion. Three monolithic prototypes are fabricated simultaneously via additive manufacturing, allowing for multiple designs to be investigated. The three transmission designs are then experimentally tested to determine the effectiveness and accuracy of the mechanism.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.035
GPT teacher head0.282
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2022
Admission routes2
Has abstractyes

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