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Record W2076247781 · doi:10.2495/hpsm080471

Waving behaviour of fatigue stress fluctuation in shrink-fit assemblies using 3D finite elements

2008· article· en· W2076247781 on OpenAlexaff
Hoa Ngan Nguyen, V. N. Le, Henri Champliaud, Floran Martin

Bibliographic record

VenueWIT transactions on the built environment · 2008
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsFinite element methodEnhanced Data Rates for GSM EvolutionMaterials scienceStress (linguistics)Structural engineeringDisplacement (psychology)BendingShear (geology)Shear stressBearing (navigation)Contact regionContact mechanicsComposite materialMechanicsEngineeringPhysics

Abstract

fetched live from OpenAlex

High bearing load roller shafts are made of high performance steels as they are subjected to severe fatigue conditions, which combine a shrink-fit load and a rotating bending. Alternating stress intensity for fatigue analysis under said conditions, using elastic and maximum shear stress theories, shows a waving behaviour within a short distance near the contact edge. This discovery is observed by using structured 3D finite element models with surface-to-surface contact and element sizes varying from a coarse size far from the contact edge down to the order of sliding displacement near the contact edge. These FE models provide proof that stresses near the contact edge increase indefinitely when element size reduces. It is also found that the maximum alternating stress intensity at the depth of one ten-thousandth of the shaft diameter, using the friction coefficient of 0.01 in FE models, gives a good comparison with empirical fatigue data of classic shrink-fit assemblies for a wide range of geometry and loading.

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 score0.347
Threshold uncertainty score0.554

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)0.0010.000

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 head0.059
GPT teacher head0.245
Teacher spread0.187 · 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
Published2008
Admission routes1
Has abstractyes

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