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Record W2241572434 · doi:10.1139/tcsme-2000-0034

CHARACTERISTICS AND MECHANICAL EFFICIENCY OF ROLADRIVES

2000· article· en· W2241572434 on OpenAlexvenueno aff
Hong‐Sen Yan, Ta-Shi Lai

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2000
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsnot available
Fundersnot available
KeywordsGear ratioMechanical designAspect ratio (aeronautics)Diameter ratioMaterials scienceMechanical engineeringStructural engineeringEngineeringComposite material

Abstract

fetched live from OpenAlex

This paper analyzes Roladrive reducers and tests their mechanical efficiency. A Roladrive is a planetary gear train that employs rollers instead of cut gears and has multiple teeth (rollers) meshed while operating. The speed ratio is independent of the pitch diameter, but it is dependent on the number of the pin-teeth and rollers. Moreover, Roladrives can be designed for noninteger speed ratios easily. When the speed ratio is greater or equal to 1 and the basic ratio is near to 2, the theoretical mechanical efficiency reaches the optimal values. Mechanical efficiency of a Roladrive is greater than 0.9 as speed ratio is less or equal to 26. The theoretical mechanical efficiency can reach as high as 0.988 when the speed ratio is equal to 2. Experimental results show that the slopes of the theoretical and measured mechanical efficiencies are very close. In conclusion, this paper provides the foundation to use the theoretical mechanical efficiency to predict the real mechanical efficiency of Roladrives.

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)0.002
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.207
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 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

Citations7
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicMechanical Engineering and Vibrations ResearchFrench-language works237,207