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Record W2580807552 · doi:10.1139/tcsme-2015-0051

A CONTACT RATIO AND INTERFERENCE-PROOF CONDITIONS FOR A SKEW LINE GEAR MECHANISM

2015· article· en· W2580807552 on OpenAlexvenueno aff
Yueling Lv, Yangzhi Chen, Xiuyan Cui

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsSkewLine (geometry)Interference (communication)Mechanism (biology)Gear ratioControl theory (sociology)MathematicsMechanical engineeringGeometryComputer sciencePhysicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Line Gear (LG) is an innovative gear which is mainly applicable to micro mechanical systems proposed by Yangzhi Chen. A Skew Line Gear Mechanism (SLGM) is one pair of LGs transmitting force and motion between two skewed axes. In this study, a design formula of a contact ratio for a SLGM is deduced, and eight influencing parameters are found. The influences of six parameters on a contact ratio for a SLGM with non-vertical skewed axes are studied by using of two coordinate parameters given definitely. The principal influencing parameters on a contact ratio for a SLGM are obtained. Moreover, two types of interferences between the driving and the driven line teeth are discussed, then these geometric parameter formulas for the interference-proof conditions are deduced, and design formulas of a maximum line tooth number for the driving line gear are derived for different interference-proof conditions.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.214
Teacher spread0.197 · 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 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

Citations9
Published2015
Admission routes1
Has abstractyes

Explore more

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