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Record W4386689710 · doi:10.4028/p-r5wvly

Analytical Modeling of Shrink-Fitted FGM Thick-Walled Cylinder

2023· article· en· W4386689710 on OpenAlexaff
Samiha Zrinej, Noreddine Laghzale, Hakim Bouzid

Bibliographic record

VenueInternational journal of engineering research in Africa · 2023
Typearticle
Languageen
FieldEngineering
TopicComposite Structure Analysis and Optimization
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsElasticity (physics)Residual stressRotational symmetryMaterials scienceMechanicsLinear elasticityFunctionally graded materialCylinderInterference fitPlane stressStructural engineeringMaterial propertiesComposite materialFinite element methodGeometryMathematicsEngineeringPhysics

Abstract

fetched live from OpenAlex

One off the most powerful assembly technique is the shrink-fitting process.It is found in many fields such us mechanics, petroleum, military industries as well as in nuclear power plants etc. This article developed an analytical formulation of shrink-fitted Functionally Graded Material axisymmetric thick-walled cylinder based on the linear plane elasticity theory. The stresses and displacement fields in the thick cylindrical shells are calculated using the laws of linear elasticity. The resulting displacements and stresses are analyzed, and particularly the residual contact pressure and her relationship with the interference values. The results show that the variation of the FGM material composition has a clear effect on the fit pressure in the intersection area of the two fitted cylinders. The value of this pressure affects the distribution of radial and tangential stresses in the FGM cylinder walls. Subsequently, we highlighted the influence of the interference value, on the residual contact pressure which increases with the increase of the interference value. The stresses are modeled for a case study using MATLAB software. keywords. shrink-fit, FGM, Interference, residual stress, Elasticity.

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)0.001
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 score0.123
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.063
GPT teacher head0.338
Teacher spread0.275 · 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
Published2023
Admission routes1
Has abstractyes

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