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Record W1983929426 · doi:10.4271/2013-01-2496

Electromechanical Clutch Actuator: Design, Analysis and Experiments

2013· article· en· W1983929426 on OpenAlexaff
Vikram Chopra, Anthony L. Smith, Jorge Angeles

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2013
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsClutchActuatorComputer scienceControl theory (sociology)Control engineeringEngineeringMechanical engineeringControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">With the aim of producing innovative clutch actuation mechanisms for automotive transmissions, we are investigating a design based on power screws. The design strives to improve clutch actuation technology and minimize energy consumption by maintaining clutch lock-up independent of an external energy source. The system consists of a lead screw shaft-and-nut assembly, a clutch apply-plate, a set of wet clutch disks and a brushless DC motor. The clutch actuation assembly is separated from the clutch-pack via thrust bearings, which allows the use of a motor, while reducing the inertial load imposed by the conventional clutch-pack. A prototype of the design was fabricated and installed on a testbed, to mimic the installation of the actuator to replace the hydraulic components. A standard 12-disk clutch-pack of an automatic transmission was used within the apparatus. The formulation of the mathematical model of the entire testbed is described in this paper. The clutch-pack stiffness is modeled as a hardening spring, to account for its elastostatic behavior. The simulated response of the model was compared to its experimental counterpart, which allowed for a rough estimate of the model parameters. Compressive force testing of the apparatus using a load cell is included.</div></div>

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.987
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.233
Teacher spread0.220 · 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.

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

Citations3
Published2013
Admission routes1
Has abstractyes

Explore more

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