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Record W1523490227 · doi:10.4271/2007-01-2297

Inverted Tooth Chain Sprocket with Frequency-Modulated Meshing Features to Reduce Camshaft Drive Noise

2007· article· en· W1523490227 on OpenAlexaff
James D. Young

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2007
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsOntario Drive & Gear (Canada)
Fundersnot available
KeywordsSprocketCamshaftNoise (video)Chain (unit)Computer scienceAutomotive engineeringAcousticsMaterials scienceMechanical engineeringEngineeringPhysicsComputer vision

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">This paper outlines the design and development of an inverted tooth style sprocket that incorporates tooth profile features to reduce chain drive noise levels by modulating or “staggering” the chain-sprocket meshing impacts. Meshing frequency modulation was achieved by replacing a number of the standard teeth with a tooth form altered to include an offset engaging flank surface to vary the location and the rhythm of the meshing impacts. This altered tooth form was arrayed with the standard teeth in a random or arbitrary pattern in order to create a random meshing sprocket, thus serving to modulate the chain meshing impact frequency compared to that of a standard inverted tooth sprocket having a full complement of symmetrical teeth.</div> <div class="htmlview paragraph">Noise and vibration tests were conducted on a 4-cylinder DOHC non-firing “motored” engine in an anechoic test cell. The tests were done to compare the camshaft drive noise levels for a 24-tooth standard crankshaft sprocket with that of a new 24-tooth random meshing sprocket. The testing confirmed the N&V gains with the random meshing sprocket at meshing frequency (24<sup>th</sup> order) and the first harmonic (48<sup>th</sup> order) as well as for the overall chain drive noise levels.</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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.990
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
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.006
GPT teacher head0.222
Teacher spread0.215 · 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 designObservational
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

Citations1
Published2007
Admission routes1
Has abstractyes

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