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Record W3168161564

Contact Stress Analysis and Optimization of Bevel Gear Pairs by Theoretical and FEA

2017· article· en· W3168161564 on OpenAlexaff
Praveen Kulkarni, Hanamant Yaragudri, Umar Farooq

Bibliographic record

VenueSSRN Electronic Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsBevel gearFinite element methodContact mechanicsStructural engineeringSpiral bevel gearStress (linguistics)EngineeringTooth surfaceNon-circular gearContact analysisMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

The gears are used to transmit the motion and power from one shaft to another shaft by physical contact between gear teeth. The bevel gear is used to transmit motion and power between the axis of intersection and the non-intersecting axis. Gears are generally subjected to loads due to these loads, tooth bending stress and contact stress will be developed on the gear tooth. Much research on the action of the gear has confirmed that contact stresses also influence the formation of pits on the surface of the tooth. Gear tooth is the most important element in a gear system and has been focused on the current study. An analysis has been carried out for the three different types of materials used to make the bevel gear. The failure of gears due to contact stress is high compared to bending stress. Stress analysis has been a key area of research to minimize failure of the gear and optimize the design. The study of contact stress developed between the mating gears are very important for the gear design. The current goal is that the finite element analysis of the bevel gear is performed to determine the maximum contact stress by ABAQUS as the solver and also the theoretical maximum contact stress is calculated by the Hertz equation. In this study finite element analysis results were validated with theoretical results.

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: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.002
GPT teacher head0.196
Teacher spread0.194 · 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
Published2017
Admission routes1
Has abstractyes

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