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Record W1932995455 · doi:10.1109/robot.1999.772523

Efficient simulation of a multilayer viscoelastic beam using an equivalent homogeneous beam

2003· article· en· W1932995455 on OpenAlexafffund
Laurent Page, I. Tremblay, Marie-Josée Potvin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsCanadian Space Agency
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsViscoelasticityFinite element methodBeam (structure)Kelvin–Voigt materialComputer scienceHomogeneousDomain (mathematical analysis)RobotFrequency domainRoboticsTime domainMechanical engineeringStructural engineeringMaterials scienceEngineeringArtificial intelligenceMathematicsPhysicsMathematical analysisComposite materialStatistical physicsComputer vision

Abstract

fetched live from OpenAlex

Addition of viscoelastic layers on flexible beam improves the behavior of flexible robots. However, the time domain simulation of such multilayer flexible beams is usually done through finite elements which makes it very time consuming. This paper proposes to build an equivalent homogenous flexible beam such that the flexible manipulator can be simulated using the variety of efficient methods developed in robotics. We propose a method to develop the equivalent Voigt-Kelvin model based on the frequency response obtained from the finite-element model. Then, we evaluate the performance of the model by simulating an experimental slewing beam covered with constrained viscoelastic material. The results are very satisfactory and the simulation is one hundred times faster than with the finite element 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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

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 science0.0000.000
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 head0.019
GPT teacher head0.244
Teacher spread0.226 · 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
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

Citations5
Published2003
Admission routes2
Has abstractyes

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