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Record W3203777629

A Study of MEMS-Based Micro-Electro-Discharge Machining and Application to Micromanufacturing

2021· article· en· W3203777629 on OpenAlexaff
Ningyuan Wang, Kenichi Takahata

Bibliographic record

VenueELEKTRIKA- Journal of Electrical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Machining and Optimization Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMicrofabricationMicroelectromechanical systemsSurface micromachiningActuatorMaterials scienceCantileverElectrical discharge machiningMachiningFabricationElectrodeVoltageProcess (computing)Mechanical engineeringElectronic engineeringNanotechnologyComputer scienceElectrical engineeringEngineeringComposite materialMetallurgy
DOInot available

Abstract

fetched live from OpenAlex

MEMS-based micro-electro-discharge machining (M3EDM) is a batch microfabrication technique that utilizes planar-electrode actuators fabricated directly on the work material. Electrostatically driving the electrode-actuator device, previously enabled with copper-based designs, using an applied EDM voltage enables micromachining of electrode patterns into any electrically conductive material. This paper presents an alternative device based on nickel with its higher thermomechanical resistance, aiming to achieve greater uniformity and stability of the process toward its application to micromanufacturing. The developed nickel-based device and its M3EDM process is used to pattern cantilever-like MEMS contact switches as a preliminary application test, demonstrating the process with the intended effects. The outcome is analyzed to suggest a need for improvement, which is addressed through a modified approach to the M3EDM fabrication of application devices showing a promising result. The study encourages further optimizations of the technology, which are evaluated and discussed as well.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.004
GPT teacher head0.225
Teacher spread0.221 · 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 designBench or experimental
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

Citations1
Published2021
Admission routes1
Has abstractyes

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Same venueELEKTRIKA- Journal of Electrical EngineeringSame topicAdvanced Machining and Optimization TechniquesFrench-language works237,207