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Record W2773307526 · doi:10.1109/smc.2017.8123008

Effect of sliding friction on transient characteristics of a gear transmission under random loading

2017· article· en· W2773307526 on OpenAlexaff
Yining Fang, Xihui Liang, Ming J. Zuo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBacklashTransient (computer programming)StiffnessNonlinear systemVibrationControl theory (sociology)Transient responseSteady state (chemistry)Displacement (psychology)Transmission (telecommunications)Dispersion (optics)MechanicsStructural engineeringEngineeringComputer sciencePhysicsAcousticsMechanical engineeringOptics

Abstract

fetched live from OpenAlex

In this paper, a nonlinear dynamic model is developed considering backlash, time-varying mesh stiffness (TVMS), and sliding friction. Friction is firstly introduced to a spur gear pair nonlinear dynamic model under random load. Transient characteristics of the spur gear transmission system are studied. The results show that random load causes longer duration of transient state and stronger vibration in steady state than deterministic load. In addition, friction plays dual roles in the gear random dynamics. Friction causes higher dispersion of the gear pair's relative angular displacement in the transient state while lower dispersion in the steady state.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.293

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 head0.007
GPT teacher head0.222
Teacher spread0.215 · 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 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

Citations5
Published2017
Admission routes1
Has abstractyes

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