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Record W2098663683 · doi:10.1115/1.4000962

Dynamic Modeling and Simulation of Percussive Impact Riveting for Robotic Automation

2010· article· en· W2098663683 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Computational and Nonlinear Dynamics · 2010
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRivetHammerProcess (computing)EngineeringAutomationBar (unit)Structural engineeringNonlinear systemFinite element methodMechanical engineeringComputer science

Abstract

fetched live from OpenAlex

Dynamic modeling and simulation of percussive impact riveting are presented for robotic automation. This is an impact induced process to deform rivets, which involves an impact rivet gun driven under pneumatic pressure to pound a rivet against a bucking bar. To model this process, first, a new approach is developed to determine the hammer output speed under input pneumatic pressure. Second, impact dynamics is applied to model the impact acting on the rivet under the hammer hits. Finally, elastoplastic analysis is carried out to derive nonlinear equations for the determination of permanent (plastic) deformations of the rivet when hitting the bucking bar. For simulation, numerical integration algorithms are applied to solve the impact dynamic model and determine the riveting time according to riveting specifications. Riveting tests are carried out for model validation. Agreement between the simulation and experimental results shows the effectiveness of the proposed 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.

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 score0.441
Threshold uncertainty score0.297

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 head0.006
GPT teacher head0.254
Teacher spread0.248 · 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