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Record W2801064707 · doi:10.1139/tcsme-2016-0019

DESIGN OF A SPHERICAL CAM-ROLLER MECHANISM FOR AN AUTOMOTIVE DIFFERENTIAL

2016· article· en· W2801064707 on OpenAlexafffundvenue
Mayank Chaudhary, Jorge Angeles, Alexei A. Morozov

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsMcGill University
FundersMcGill University
KeywordsMechanism (biology)Bevel gearBacklashPressure angleAutomotive industryBevelSpiral bevel gearDifferential (mechanical device)PinionMechanical engineeringGravitational singularityEngineeringAutomotive engineeringComputer sciencePhysicsMathematicsMathematical analysisAerospace engineering

Abstract

fetched live from OpenAlex

Spherical cam-follower mechanisms are attractive alternatives to bevel gears, as they provide low backlash and low friction losses. The design of such a mechanism is reported here, to be incorporated in an automotive differential, whose bevel pinions and side gears are substituted by spherical cams and rollercarriers, respectively. Critical to the design of cam mechanisms is the generation of the cam-profile, free of undercutting. The profile, generated using computer algebra, is analyzed for singularities, including cusps and double points. The pressure angle, an important factor that governs the effective force transmission of cam mechanisms, is duly kept within acceptable limits. Conclusions are drawn on the suitability of cam mechanism developed for automotive applications.

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)0.000
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.027
GPT teacher head0.235
Teacher spread0.209 · 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
Published2016
Admission routes3
Has abstractyes

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Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicMechanical Engineering and Vibrations ResearchFrench-language works237,207