MétaCan
Menu
Back to cohort
Record W4400083340 · doi:10.1080/15397734.2024.2369949

An investigation into the effects of profile shifts on helical gear mesh stiffness

2024· article· en· W4400083340 on OpenAlexaff
Ruoyi Wang, Jinhai Wang, Jianwei Yang, Yuping He, Dechen Yao

Bibliographic record

VenueMechanics Based Design of Structures and Machines · 2024
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsOntario Tech University
FundersNational Natural Science Foundation of China
KeywordsStiffnessStructural engineeringMaterials scienceAcousticsEngineeringOrthodonticsPhysicsMedicine

Abstract

fetched live from OpenAlex

Time-varying meshing stiffness (TVMS) is a significant nonlinear periodic excitation in helical gears, it poses significant impacts on gear system’s dynamic characteristics and fatigue life. However, the specific impact of profile shift on it remains unclear. This article establishes an analytical model for the TVMS of helical gears by utilizing the slicing method to address this gap based on the potential energy method, and quantitatively analyze the TVMS with different modification coefficients. The research found that the helical gear’s TVMS decreases with the increase of modification coefficient for individual profile shifts. For the equal compound profile shifts, it rises with the increase of modification coefficient of pinion. For the unequal compound profile shifts, the positive drive increases the TVMS, but the negative drive to the contrary. The research achievements provide valuable guidelines for dynamic design and structure optimization for helical gears.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.214
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueMechanics Based Design of Structures and MachinesSame topicGear and Bearing Dynamics AnalysisFrench-language works237,207