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Record W2080016403 · doi:10.1115/imece2006-15379

Modeling and Analytical Study of Scratch Drive Actuators

2006· article· en· W2080016403 on OpenAlexaff
Peyman Honarmandi, Jean W. Zu, Kamran Behdinan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
Fundersnot available
KeywordsActuatorScratchDeflection (physics)Mode (computer interface)VoltageBoundary value problemBendingMechanicsBeam (structure)Mechanical engineeringComputer scienceStructural engineeringOpticsMaterials scienceEngineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents a new analytical model of Scratch Driver Actuator (SDA) to obtain the design parameters of this device. In order to simulate working states of the SDA, three different static modes are considered: non-contact mode, transition mode, and full-contact mode. The SDA is simplified as an L-shape beam with different boundary conditions in each mode. Using beam theories in bending, the governing equation is derived and solved in each mode. The non-contact mode allows identifying the deflection and the driving voltage after which the actuator plate starts touching the substrate. From the transition mode, we calculate the primary contact length to be used for the analysis of the full-contact mode. In the full-contact mode, the SDA is completely snapped down in its working cycle. From this mode, we obtain the relationships between input voltage and design parameters such as SDA geometry, contact length, and step size. It is shown that a good agreement between theoretical results and experimental values is achieved, which verifies the validity of the new model.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.009
GPT teacher head0.232
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), 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
Published2006
Admission routes1
Has abstractyes

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