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Record W3117968590 · doi:10.1115/detc2000/ptg-14394

Feature Based Numerical Bearing Pattern Development and Optimization for Spiral-Bevel and Hypoid Gears

2000· article· en· W3117968590 on OpenAlexaff
Claude Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSpiral bevel gearBevel gearSpiral (railway)BevelBearing (navigation)Point (geometry)Feature (linguistics)EngineeringComputer scienceMechanical engineeringStructural engineeringGeometryMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract A novel approach to the development of the bearing pattern for spiral-bevel and hypoid gears is presented. The numerical method uses the concept of “potential point of contact” to determine the shape of the separation between the meshing tooth surfaces in the vicinity of the Mean Point. Machine settings are used as control parameters in the numerical solution to modify the shape and location of the bearing pattern. The presented method, which has been used in the automobile industry for several years, allows substantial freedom in the development of spiral bevel and hypoid gear sets, even on conventional generators.

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 score0.512
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.007
GPT teacher head0.186
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 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

Citations0
Published2000
Admission routes1
Has abstractyes

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