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Record W2283904298 · doi:10.1139/tcsme-2005-0003

CONFIGURATION DESIGN OF SIX-SPEED AUTOMATIC TRANSMISSIONS WITH TWO-DEGREE-OF-FREEDOM PLANETARY GEAR TRAINS

2005· article· en· W2283904298 on OpenAlexvenueno aff
Wen-Mlln Hwang, Yu-Lien Huang

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2005
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGear trainTrainClutchGear ratioSet (abstract data type)Computer scienceAutomatic transmissionControl theory (sociology)Degree (music)AlgorithmEngineeringAutomotive engineeringBacklashArtificial intelligenceAcousticsPhysics

Abstract

fetched live from OpenAlex

A methodology is proposed herein to search for and synthesize feasible configurations of six-speed automatic transmissions based on planetary gear trains with two degrees of freedom. A screening rule is first proposed to search for workable two-degree-of-freedom planetary gear trains for six-speed automatic transmissions. Based on the requirement of clutch-to-clutch shifts, a new algorithm to classify and permute speed ratios for feasible clutching sequences is presented. Six feasible configurations of six-speed automatic transmissions are found using the proposed methodology. By using an exhaustive search method, the ten best combinations of the number of gear teeth for a specific planetary gear train are then obtained to minimize the differences between the set of desired speed ratios and that of speed ratios generated algorithmically. Finally, the mechanical efficiencies of the ten planetary gear trains are calculated for evaluation.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.195
Teacher spread0.179 · 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

Citations20
Published2005
Admission routes1
Has abstractyes

Explore more

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