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Record W3120651742 · doi:10.1177/0954406220983365

Coupling effects of manufacturing error and flexible ring gear rim on dynamic features of planetary gear

2021· article· en· W3120651742 on OpenAlexaff
Zheng Cao, Meng Rao

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · 2021
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCoupling (piping)Ring (chemistry)Differential (mechanical device)Non-circular gearGear trainProperty (philosophy)Timoshenko beam theoryComputer scienceSpur gearMechanical engineeringStructural engineeringEngineeringBeam (structure)Spiral bevel gearAerospace engineering

Abstract

fetched live from OpenAlex

Manufacturing errors widely exist in and deteriorate the dynamic property of planetary gear train (PGT). To solve this problem, the ring gear is often designed with a thin rim to compensate for the effects of manufacturing errors via the elastic deflections of the rim. Existing dynamic models of the PGT only consider the effects of either the elasticity of the rim of the ring gear or the manufacturing errors. The coupling effects of manufacturing errors and the flexible ring gear are ignored. To understand the dynamic behaviors of the PGT better, a dynamic model of the PGT coupled with typical manufacturing error and flexible ring gear is developed in this study. The tooth contact analysis of the ring-planet mesh, which is calculated based on the potential energy method and uniformly curved Timoshenko beam theory, is studied using the influence coefficient method. A numerical algorithm is proposed to solve the integrated dynamic equations of the PGT. Calculated results show that the dynamic features of the PGT are complex, and the load sharing characteristic is improved when the flexible ring gear is incorporated.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.175
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.209
Teacher spread0.202 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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Same venueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering ScienceSame topicGear and Bearing Dynamics AnalysisFrench-language works237,207