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Record W2057171652 · doi:10.1115/1.3125206

Kinematics and Dynamics of a Self-Stressed Cartesian Cable-Driven Mechanism

2009· article· en· W2057171652 on OpenAlexaff
Saeed Behzadipour

Bibliographic record

VenueJournal of Mechanical Design · 2009
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsKinematicsStiffnessActuatorCartesian coordinate systemMechanism (biology)Control theory (sociology)Redundancy (engineering)Compliant mechanismEngineeringComputer scienceStructural engineeringPhysicsMathematicsClassical mechanicsGeometryFinite element method

Abstract

fetched live from OpenAlex

A novel hybrid cable-driven mechanism with Cartesian motion is introduced. It consists of a rigid-link Cartesian mechanism and a cable drive system with stationary actuators to provide the motion. The cable drive system is self-stressed meaning that the tension in the cables required to keep them taut is provided internally and hence does not depend on either actuation redundancy or external sources such as gravity. This keeps the number of actuators at minimum and also eliminates the static loading of actuators as well as redundant work. The kinematic analysis of the mechanism is presented. The forward and inverse kinematic solutions are found and shown to be linear and pose independent. Also the stiffness of the mechanism induced by the compliance of the cable is analyzed to find the weakest stiffness. For this purpose, a parameter called “compliance length” is defined and used. Compliance length presents the stiffness of the mechanism by the length of the cable with the same stiffness. It makes the analysis independent from the design and the properties of the cable and can be quite useful in the design process. Finally, two dynamic models are given for the mechanism depending on whether or not the cable is stretchable and the properties of each model are discussed.

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 score0.368
Threshold uncertainty score0.737

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 head0.010
GPT teacher head0.205
Teacher spread0.195 · 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
Published2009
Admission routes1
Has abstractyes

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