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Record W2121468671 · doi:10.1115/1.4003849

Stiffness Analysis of a 2DOF Planar Tensegrity Mechanism

2011· article· en· W2121468671 on OpenAlexaff
Marc Arsenault

Bibliographic record

VenueJournal of Mechanisms and Robotics · 2011
Typearticle
Languageen
FieldEngineering
TopicStructural Analysis and Optimization
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsTensegrityStiffnessMechanism (biology)PlanarWorkspaceStructural engineeringDirect stiffness methodStiffness matrixCompliant mechanismComputer scienceEngineeringMaterials scienceFinite element methodPhysicsRobotArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents the stiffness analysis of a planar 2DOF tensegrity mechanism. A stiffness model is first derived based on an existing formulation. Several stiffness indices having physical meaning are then extracted from the stiffness matrix for performance evaluation purposes. Stiffness mappings based on these stiffness indices are then plotted over the mechanism’s workspace and observations are made. It is shown, for instance, that in the case of the planar 2DOF tensegrity mechanism, the effect of the prestress on the stiffness is generally not significant when the stiffnesses of the cables and struts are assumed to be linear.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.195
Teacher spread0.178 · 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

Citations11
Published2011
Admission routes1
Has abstractyes

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