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Record W1588522392 · doi:10.1109/icmens.2004.1509026

Modeling of Surface Forces between Micron-Sized Objects in Dry Condition

2006· article· en· W1588522392 on OpenAlexaff
Alireza Hariri, Jean W. Zu, R. Ben Mrad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProbability density functionSurface (topology)Surface roughnessSurface finishFractalUpper and lower boundsGaussianMachiningSurface forceMechanicsMaterials scienceMathematicsMathematical analysisGeometryPhysicsComposite materialStatistics

Abstract

fetched live from OpenAlex

Capillary, van der Walls (vdW) and electrostatic forces, which usually termed as surface forces, can significantly affect the behavior and performance of Micro Electro Mechanical Systems (MEMS) containing surfaces that can contact each other. Here, we are concerned with vdW force, which is the dominant surface force between conducting surfaces in the dry condition. In this study, we first review existing roughness models described by stochastic processes of Gaussian and Fractal type. Then, the vdW force is formulated using two methods by considering the first and second order probability density function (pdf) of the height distribution of rough surfaces. The resulting formulae are functions of the correlation ( ñ) between successive sampling points. By analyzing these formulae based on the correlation and other parameters, the upper and lower bound of vdW force are identified and a numericalbased closed-form formula for the upper bound is derived. Finally, various situations are discussed based on the developed equations and data from a surface micro machining process.

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 score0.049
Threshold uncertainty score0.410

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 head0.009
GPT teacher head0.226
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
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

Citations4
Published2006
Admission routes1
Has abstractyes

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