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Record W2031243301 · doi:10.1115/detc2012-70207

Nonlinear Oscillation of Shell Workpiece in High Speed Milling Under 1:2 Internal Resonance Condition

2012· article· en· W2031243301 on OpenAlexaff
Wei Zhang, Rui Zhou, Jean W. Zu, Qian Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGalerkin methodNonlinear systemShell (structure)CantileverMultiple-scale analysisParametric oscillatorEquations of motionNonlinear resonanceOrdinary differential equationResonance (particle physics)MechanicsChaoticDisplacement (psychology)Differential equationClassical mechanicsPerturbation (astronomy)Mathematical analysisPhysicsMathematicsMaterials scienceStructural engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

We aim to study nonlinear dynamics of a shell-shaped workpiece during milling processes in this paper. The shell-shaped workpiece is modelled as a cantilever thin shell subjected to a cutting force with time-delay effects. The formulas of the cantilever shell were derived by the classical shell theory and the von Karman strain-displacement relations. The resulting differential equations are reduced to a two-degree-of-freedom nonlinear system ordinary differential equations by applying the Galerkin’s approach. The method of Asymptotic Perturbation method is used to obtain the averaged equations, which were dealt with the resonance cases of 1:2 internal resonance and principal parametric resonance. Dynamic behaviors are presented based on the numerical solutions. The results show that different time-delay parameters result in periodic motion, multiple periodic motion, and chaotic motion.

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 score0.410
Threshold uncertainty score0.286

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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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