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Record W2150311140 · doi:10.1109/icma.2010.16

A Comparison Study of Three Degree-of-Freedom Micro-motion Parallel Kinematic Machines with/without Actuation Redundancy

2010· article· en· W2150311140 on OpenAlexafffund
Dan Zhang, Qi Shi, Jian Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRedundancy (engineering)ActuatorKinematicsControl theory (sociology)Jacobian matrix and determinantInverse kinematicsStiffnessMotion controlParallel manipulatorComputer scienceEngineeringRobotMathematicsArtificial intelligencePhysicsStructural engineeringClassical mechanics

Abstract

fetched live from OpenAlex

This paper presents a comparison study of two unique micro-motion parallel kinematic manipulators (PKM): a three degrees of freedom (DOF) micro-motion PKM and a 3-DOF micro-motion PKM with actuation redundancy. The 3-DOF micro-motion PKM has three linear-motion driving units, while the 3-DOF micro-motion PKM with actuator redundancy has four of these units. For both designs, the linear motion driving units are identical and both machines have the same passive link in the middle of the structure. The manipulator with actuator redundancy is designed to prevent singularity and to improve stiffness. The inverse kinematics and Jacobian of these two PKMs are analyzed, the stiffness properties are determined and global stiffness is optimized using genetic algorithm. A comparison of the global stiffness indicates that the 3-DOF micro motion PKM with actuator redundancy outperforms the one without actuator redundancy.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.251
Teacher spread0.233 · 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

Citations2
Published2010
Admission routes2
Has abstractyes

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