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Record W4287992835 · doi:10.5281/zenodo.3600035

NEW APPROACH BASED ON ANALYTIC - NUMERICAL METHOD FOR CALCULATING THE SLIDING DISTANCE OF TEETH PROFILE POINTS DURING GEARS MESHING

2019· article· en· W4287992835 on OpenAlexaff
D. Koffi N. Agbetossou, K. Attipou, S. Tiem and A. Afio

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsComputer scienceStructural engineeringGeologyGeometryMathematicsEngineering

Abstract

fetched live from OpenAlex

New approach based on analytic-numerical method for calculating the sliding distance of teeth profile points during gears meshing is proposed in this study. In our approach we have shown that the sliding distance of teeth profile points is due to the Hertz deformation. Our method of calculation consist of following a point on the profile by its radius during the contact. The analytical approach has been successfully implemented in Matlab code and solved numerically. The results show that the sliding distance is not null at pitch point. Compared with the common used method, the results are very close, except at the neighborhoods of the pitch point and the points of the head and the foot, where the deviations become greater. The deviations for normal points vary between 0.16% and 6.52% for simulated meshing conditions.

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 categoriesInsufficient payload (model declined to judge)
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 score0.921
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.244
Teacher spread0.221 · 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 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
Published2019
Admission routes1
Has abstractyes

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