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Record W2348498838 · doi:10.1177/1687814016638824

Optimized use of cooling holes to decrease the amount of thermal damage on a plastic gear tooth

2016· article· en· W2348498838 on OpenAlexaff
Demagna Koffi, Alencar Bravo, Lotfi Toubal, Fouad Erchiqui

Bibliographic record

VenueAdvances in Mechanical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsUniversité du Québec en Abitibi-TémiscamingueUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsMaterials scienceThermal conductionDeflection (physics)ThermalHeat deflection temperatureComposite materialMechanical engineeringStructural engineeringMechanicsEngineeringThermodynamicsOpticsUltimate tensile strength

Abstract

fetched live from OpenAlex

The full potential of plastic gear usage is limited by not only poor mechanical properties but also equally poor temperature limits and poor heat conduction properties. Cooling holes were developed to decrease the amount of thermal damage on the contact surface. These cooling holes promote increased stress and tooth deflection, thus exerting a negative effect. This article compares various cooling holes for plastic gear configurations and proposes novel cooling holes. Thermal and mechanical simulations that consider specific aspects of plastic gear meshing were performed. The main objective of this article was to verify the best methods for reducing thermal damage through cooling holes. The results indicate the best compromise between the temperature reduction and the mechanical properties of the new tooth geometry. The results also indicate that the simple variations in the cooling holes proposed can improve tooth performance.

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)0.001
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: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.212
Teacher spread0.205 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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