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Record W2462729781 · doi:10.1139/tcsme-2001-0002

LOAD ANALYSIS OF MISALIGNED GEAR COUPLING USING THE CLEARANCE DISTRIBUTION OF MESHING TEETH

2001· article· en· W2462729781 on OpenAlexvenueno aff
Mohammed Alfares, Ahmed Elkholy

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2001
Typearticle
Languageen
FieldEngineering
TopicMechanical Failure Analysis and Simulation
Canadian institutionsnot available
Fundersnot available
KeywordsCoupling (piping)Structural engineeringLoad sharingCircumferenceRotation (mathematics)Load distributionPosition (finance)Distribution (mathematics)Materials scienceEngineeringComputer scienceGeometryMechanical engineeringMathematicsMathematical analysisComputer network

Abstract

fetched live from OpenAlex

A gear coupling is one of the most common types of couplings in use today. Its popularity is due to its ability to accommodate axial, radial and angular misalignments which result in uneven distribution and sharing of transmitted loads between meshing teeth and may enhance deterioration in the coupling due to fatigue if not accounted for. In this study, load sharing among coupling teeth in the presence of angular misalignment is investigated. A procedure is introduced in which the coupling teeth are modelled as linear springs whose stiffnesses are equal and determinable from the coupling geometry and material. It was found out that the external male teeth are separated from the corresponding female internal teeth by distances (clearances) determined from the backlash and the angle of misalignment. Therefore, when the coupling is loaded, the teeth with small clearances come into contact before the ones with larger clearances. This results in an uneven distribution of loads among contacting teeth where some teeth may be completely unloaded or heavily loaded; depending upon the angle of misalignment, backlash and their clearance distribution. This produces cyclic loading on teeth during coupling rotation and may result in failure by fatigue.

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 score0.767
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.223
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 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
Published2001
Admission routes1
Has abstractyes

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Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicMechanical Failure Analysis and SimulationFrench-language works237,207