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Record W4400899285 · doi:10.1051/e3sconf/202455201101

Hysteresis Stress, Strain and Penetration Analysis of Spur Gear Assembly for Various Sustainable Design

2024· article· en· W4400899285 on OpenAlexaff
Bharat Singh, Shaymaa A. Ahmed, J. Sridevi, B Rajalakshmi, H Pal Thethi, Abhishek Kaushik, Vemuri Venkata Phani Babu

Bibliographic record

VenueE3S Web of Conferences · 2024
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsSpur gearHysteresisPenetration (warfare)SpurStructural engineeringStress (linguistics)Mechanical engineeringComputer scienceMaterials scienceEngineering

Abstract

fetched live from OpenAlex

This paper considers and compares the hysteresis stress and strain and the penetration property of spur gear assemblies based on three unique designs. Spur gear plays an important part in mechanical structures, and any mechanical setup should consider the execution of such a mechanical component under distinct designs to improve its mechanical productivity and sustainability. To explore the ways in which the mechanical behaviour of the designs varies with the design configurations, we integrate simulation analysis with an experimental study. The outcomes of this paper indicate considerable differences in both hysteresis stress, strain distribution, and penetration behavior measurements between three designs. The paper explains the stated disparities by the unique geometric layouts and material characteristics of each design. Furthermore, it emphasizes that some of the examined designs have lower hysteresis losses and favourable stress and strain distributions, which positively affects the long-term performance of gear systems. Other designs, however, exhibit severe penetration and stress concentrations leading to rapid gear wear and likely premature failure. In distinguishing these events, the present study offers a valuable approach to the parameters that influence the performance of gear systems and aids in the improvement of the design methodology.

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 score0.315
Threshold uncertainty score0.347

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 head0.015
GPT teacher head0.237
Teacher spread0.222 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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