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Record W2800739767 · doi:10.1139/tcsme-2009-0024

AN INDEPENDENT ACTIVE TORQUE BALANCER USING A SERVO-CONTROLLED DIFFERENTIAL GEAR TRAIN

2009· article· en· W2800739767 on OpenAlexvenueno aff
Yan‐an Yao

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsTorqueControl theory (sociology)ServomotorPoint (geometry)Differential (mechanical device)EngineeringControl engineeringMechanism (biology)Computer scienceControl (management)MathematicsMechanical engineeringArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

This paper proposes a novel concept of active balancer for reducing the input torque fluctuations of mechanisms. A differential gear train is used in this active balancer and one of its two input shafts is driven and controlled by a servomotor. From the structural point of view, it is designed as an independent device that can be assembled and disassembled easily; from the functional point of view, it can minimize the torque fluctuations in a variety of working conditions. At first, an exact control function of the servomotor that can totally eliminate the input torque fluctuations of the mechanism is gained by an analytical method; in what follows, an optimization approach is developed to select appropriate control functions for the servomotor to balance the input torque of the working mechanism with consideration of the servomotor’s own input torque minimization; finally, an integrated method is presented for optimizing both the control function of the servomotor and the structure parameters of the differential gear train. Two numerical examples are given to illustrate the design procedure and to show its feasibility.

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 score0.907
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.014
GPT teacher head0.241
Teacher spread0.227 · 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

Citations5
Published2009
Admission routes1
Has abstractyes

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