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Record W1967673364 · doi:10.1115/1.4024366

Design of Parallel Mechanisms for Flexible Manufacturing With Reconfigurable Dynamics

2013· article· en· W1967673364 on OpenAlexafffund
Gianmarc Coppola, Dan Zhang, Kefu Liu, Zhen Gao

Bibliographic record

VenueJournal of Mechanical Design · 2013
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsLakehead UniversityOntario Tech University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsKinematicsRobotDegrees of freedom (physics and chemistry)Computer scienceWork (physics)Multibody systemParallel manipulatorProperty (philosophy)Control engineeringMechanism (biology)SimulationDynamics (music)EngineeringMechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Reconfigurable robotic systems can enhance productivity and save costs in the ever growing flexible manufacturing regime. In this work, the idea to synthesize robotic mechanisms with dynamic properties that are reconfigurable is studied, and a methodology to design reconfigurable mechanisms with this property is proposed, named reconfigurable dynamics (Re-Dyn). The resulting designs have not only the kinematic properties reconfigurable, such as link lengths, but also properties that directly affect the forces and accelerations, such as masses and inertias. A 2-degree of freedom (DOF) parallel robot is used as a test subject. It is analyzed and redesigned with Re-Dyn. This work also presents the robots forward dynamic model in detail, which includes the force balancing mediums. The connection method is directly utilized for this derivation, which is well suited for multibody dynamics and provides insight for design parameters (DPs). Dynamic performance indices are also briefly discussed as related to the Re-Dyn method. After redesigning the robot, a full simulation is conducted to compare performances related to a flexible manufacturing situation. This illustrates the advantages of the proposed method.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.196
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.024
GPT teacher head0.211
Teacher spread0.187 · 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
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

Citations18
Published2013
Admission routes2
Has abstractyes

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