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Record W1639729015 · doi:10.1063/1.2011298

Non-uniform Pressure Distribution in Draw-Bend Friction Test and its Influence on Friction Measurement

2005· article· en· W1639729015 on OpenAlexaff
Young Seok Kim

Bibliographic record

VenueAIP conference proceedings · 2005
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceTension (geology)Finite element methodMechanicsPulleyFriction coefficientComposite materialSheet metalStructural engineeringEngineeringCompression (physics)Physics

Abstract

fetched live from OpenAlex

From various draw‐bend friction tests with sheet metals at lubricated conditions, it has been unanimously reported that the friction coefficient increases as the pin diameter decreases. However, a proper explanation for this phenomenon has not been given yet. In those experiments, tests were performed for different pin diameters while keeping the same average contact pressure by adjusting applied tension forces. In this paper, pressure profiles at pin/strip contacts and the changes in the pressure profiles depending on pin diameters are investigated using finite element simulations. To study the effect of the pressure profile changes on friction measurements, a non‐constant friction model (Stribeck friction model), which is more realistic for the lubricated sheet metal contacts, is implemented into the finite element code and applied to the simulations. The study shows that the non‐uniformity of the pressure profile increases and the pin/strip contact angle decreases as the pin diameter decreases, and these phenomena increase the friction coefficient, which is calculated from the strip tension forces using a conventional rope‐pulley equation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score0.635

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 head0.019
GPT teacher head0.218
Teacher spread0.199 · 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

Citations5
Published2005
Admission routes1
Has abstractyes

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