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Record W2084795752 · doi:10.1115/imece2004-60486

A Generalized Impact-Sliding Fretting Wear Model Based on Fracture Mechanics Approach

2004· article· en· W2084795752 on OpenAlexaff
Yemane B Gessesse, Helmi Attia, M. O. M. Osman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsConcordia UniversityMcGill UniversityNational Research Council Canada
Fundersnot available
KeywordsFrettingMaterials scienceFracture (geology)MechanicsTribologyFracture mechanicsFlow (mathematics)Structural engineeringEngineeringMetallurgyComposite materialPhysics

Abstract

fetched live from OpenAlex

Impact-sliding fretting wear is a complex phenomenon due to the random nature of the flow-induced vibrations, and the self-induced tribological changes. Available models, which relate wear losses to the process variables, are empirical in nature and bear no physical similarity to the actual mathematical and physical attributes of the wear process. A generalized model is developed in the present work to mathematically describe the fretting wear process under various modes of motion, namely, impact, sliding and oscillatory. This model, which is based on the findings from the fracture mechanics analysis of the crack initiation and propagation processes, takes into consideration the simultaneous action of both the surface adhesion and subsurface fatigue mechanisms. The model also accounts for the micro-, and macro- contact configuration of the tube-support system. The closed form solution requires the calibration of single parameter, using a limited number of experiments, to account for the effect of environment and the support material. The model was validated using experimental data that are generated for Inconel 600 and Incology 800 tube materials at room and high temperature environment, and for different types of motion. The results showed that model can accurately predict wear losses within a factor of < ±3. This narrow range presents better than an order of magnitude improvement over the current state-of-the-art models.

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: Methods · Consensus signal: none
Teacher disagreement score0.921
Threshold uncertainty score0.697

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 head0.017
GPT teacher head0.234
Teacher spread0.217 · 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
GenreMethods

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

Citations6
Published2004
Admission routes1
Has abstractyes

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