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Record W1983081867 · doi:10.4271/2013-36-0551

Technical and Economic Advantages of Cold Forged Planetary-Bevel Gears Developed with Net Shape Teeth and Splines

2013· article· en· W1983081867 on OpenAlexaff
Juliano Savoy, Mauro Moraes de Souza, Tadeu Geraldo Domingues, Paulo César Sigoli

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2013
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsWeyerhauser (Canada)
Fundersnot available
KeywordsBevel gearNet (polyhedron)BevelMechanical engineeringEngineering drawingComputer scienceEngineeringStructural engineeringMathematicsGeometry

Abstract

fetched live from OpenAlex

The search for more technical and economical competitive automotive products motivates even more the engineers to research for solutions that reduce manufacturing costs and lead-time. Bevel gears are applied extensively in the automotive industry since the invention of the transmission differential, however; few changes of the design have been done on these components in the last decades. Currently, the planetary-bevel gear blanks are hot forged with posterior cutting of the teeth and broaching of the spline, eventually, some planetary-bevel gear blanks have the teeth warm forged. The process to cold forge the teeth and the splines results as much technically benefits for the product application as manufacturing costs and lead-time reductions. This paper presents a planetary-bevel gears manufacturing concept for passenger and light commercial vehicles, where the cold forged teeth and splines present technical and economic benefits to the automotive differential transmission system.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.009
GPT teacher head0.209
Teacher spread0.200 · 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 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

Citations2
Published2013
Admission routes1
Has abstractyes

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