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Record W2913528039 · doi:10.1139/tcsme-2017-0029

Meshing performance of spiral bevel gear with different loads and modules considering edge contact by finite element method

2018· article· en· W2913528039 on OpenAlexvenueno aff
Xiangying Hou, Zongde Fang, Xuezhong Fu, Xijin Zhang

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsnot available
FundersNatural Science Foundation of Shaanxi ProvinceNational Natural Science Foundation of China
KeywordsFinite element methodEnhanced Data Rates for GSM EvolutionContact mechanicsStress (linguistics)Contact analysisPosition (finance)Transmission (telecommunications)Contact areaStructural engineeringBendingBevelBevel gearNode (physics)Spiral (railway)Computer scienceEngineeringMaterials scienceMechanical engineeringElectrical engineeringComposite material

Abstract

fetched live from OpenAlex

To analyze the edge contact of spiral bevel gears, owing to its effect on meshing performance, batch processing is performed based on a static solver and the finite element method (FEM) because of its computational speed advantage. A series of programs automatically perform the functions of modeling, analysis, and extraction of performance indexes. Starting from the entrance meshing position to the exit meshing position, a series of models was built and analyzed to describe the whole meshing process of the gear pair. For a specific gear pair, contact and bending stresses, contact stress patterns, loaded transmission errors, contact forces, and load sharing coefficients are calculated under five different load conditions and the change rules are summarized. Edge contact phenomenon occurs as the load increases to a critical value, resulting in a sharp increase in contact stress. As the load increases, the load sharing coefficient decreases gradually and the absolute value of transmission error increases, but the fluctuation of transmission error first decreases and then increases. In addition, the effect of modules is discussed and the results show that large modulus will decrease stress, contact ratio, and edge contact, but there is a certain module to minimize the fluctuation of transmission error.

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: none
Teacher disagreement score0.581
Threshold uncertainty score0.486

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 head0.009
GPT teacher head0.197
Teacher spread0.188 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicGear and Bearing Dynamics AnalysisFrench-language works237,207