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Record W2321081656 · doi:10.1115/detc2004-57140

On the Design of a 3-PRRR Spatial Parallel Compliant Mechanism

2004· article· en· W2321081656 on OpenAlexafffund
Yang Wang, Clément Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsWorkspaceBacklashMechanism (biology)KinematicsCompliant mechanismCompensation (psychology)Computer scienceControl theory (sociology)Position (finance)Transmission (telecommunications)Range (aeronautics)Orientation (vector space)Motion controlSimulationControl engineeringEngineeringArtificial intelligenceControl (management)Finite element methodRobotPhysicsMathematicsGeometryStructural engineeringAerospace engineering

Abstract

fetched live from OpenAlex

Because wear, clearance and backlash can be reduced or eliminated, one of the important advantages of compliant mechanisms is their potential to increase the mechanism precision dramatically. In this paper, a spatial compliant mechanism based on the 3-PRRR parallel mechanism driven by linear motors is presented. The design methodology is described and the kinematic and static models are developed. Using the “transmission ratio”, an effective and simple compensation method is designed. The performances of this mechanism are studied with the help of the structure simulation module in Pro-Mechanica. Using an open-loop control with compensation, measurements over a 20×20×20 mm3 workspace show that the position accuracy is smaller than 1 μm, i.e., 0.05% of the full range of motion and that the orientation error is less than 1 μradian.

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 score0.992
Threshold uncertainty score0.187

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 head0.183
Teacher spread0.170 · 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

Citations8
Published2004
Admission routes2
Has abstractyes

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