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Record W4388642425 · doi:10.1109/lra.2023.3332501

Model-Based Co-Simulation of Flexible Mechanical Systems With Contacts Using Reduced Interface Models

2023· article· en· W4388642425 on OpenAlexafffund
Xu Dai, Ali Raoofian, József Kövecses, Marek Teichmann

Bibliographic record

VenueIEEE Robotics and Automation Letters · 2023
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInterface (matter)Flexibility (engineering)Computer scienceCo-simulationScheme (mathematics)Mechanical systemInformation exchangeRigid bodyData exchangeWork (physics)SimulationControl engineeringDistributed computingMechanical engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Co-simulation is a useful approach in the modelling of robotic systems composed of multiple parts. In co-simulation, the subsystems only exchange information at communication points. The time delay of information exchange may cause error and instability. Thus, an appropriate way to determine the interface variables between the communication points is essential for efficient and stable performance, especially for real-time applications. Reduced interface models (RIMs) can be used to represent the dynamic behaviour of the subsystems at the interface in co-simulation. Such a model-based co-simulation scheme was limited to systems consisting of rigid bodies in previous studies. In this work, we introduce the formulation of RIMs for flexible multibody systems and based on that propose a general co-simulation scheme for systems consisting of both rigid body components and elements with structural flexibility. A robotic model is employed as an example to demonstrate the co-simulation scheme, where a non-smooth subsystem with contact interactions is present. The advantages of constructing RIM using flexible mechanical system models over rigid body models are also addressed by comparing the effective mass properties and the simulation results.

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: none
Teacher disagreement score0.653
Threshold uncertainty score0.493

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 head0.032
GPT teacher head0.258
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

Citations8
Published2023
Admission routes2
Has abstractyes

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