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Record W2322800571 · doi:10.1115/detc2008-49658

A Hybrid Self-Stressed Cable-Driven Mechanism

2008· article· en· W2322800571 on OpenAlexaff
Saeed Behzadipour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMechanism (biology)ActuatorWorkspaceStiffnessKinematicsControl theory (sociology)Tension (geology)Cartesian coordinate systemWork (physics)Computer scienceEngineeringStructural engineeringRobotMechanical engineeringPhysicsElectrical engineeringMathematicsCompression (physics)

Abstract

fetched live from OpenAlex

A new hybrid cable-driven manipulator is introduced. The manipulator is composed of a Cartesian mechanism to provide three translational degrees of freedom and a cable system to drive the mechanism. The end-effector is driven by three rotational motors through the cables. The cable drive system in this mechanism is self-stressed meaning that the pre-tension of the cables which keep them taut is provided internally. In other words, no redundant actuator or external force is required to maintain the tensile force in the cables. This simplifies the operation of the mechanism by reducing the number of actuators and also avoids their continuous static loading. It also eliminates the redundant work of the actuators which is usually present in cable-driven mechanisms. Forward and inverse kinematics problems are solved and shown to have explicit solutions. Static and stiffness analysis are also performed. The effects of the cable’s compliance on the stiffness of the mechanism is modeled and presented by a characteristic cable length. The characteristic cable length is calculated and analyzed in representative locations of the workspace.

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 score0.007

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 communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.169
Teacher spread0.161 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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