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Record W2040871942 · doi:10.1109/memsys.2009.4805480

MEMS-Based Batch-Mode Micro-Electro-Discharge Machining using Microelectrode Arrays Actuated by Hydrodynamic Force

2009· article· en· W2040871942 on OpenAlexafffund
Chakravarty Reddy Alla Chaitanya, Kenichi Takahata

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Machining and Optimization Techniques
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicroelectrodeMaterials scienceCapacitanceElectrodeMicroelectromechanical systemsMachiningElectrical discharge machiningSurface micromachiningPlanarVoltageOptoelectronicsLayer (electronics)Composite materialElectrical engineeringFabricationMetallurgyEngineeringComputer science

Abstract

fetched live from OpenAlex

This paper reports a batch-mode micro-electro-discharge machining technique that is enabled by the actuation of the suspended microelectrode arrays fabricated on the workpiece using the flow of the machining fluid. The electrode devices are microfabricated to be the double-layer construction that has 25-mum-thick copper electrodes with custom patterns on the bottom of the 18-mum-thick planar structures suspended above the workpiece surfaces. The built-in capacitance of the electrode device is utilized to construct a resistance-capacitance pulse generation/timing circuit. The suspended planar electrodes are advanced into the workpiece material with the hydrodynamic force while sustaining high-frequency discharge pulses, removing the workpiece material. Arrays of microstructures with 26-mum depth were machined in stainless steel using the machining voltage of 90 V. The dynamic behavior of the built-in capacitance of the double-layer electrode devices with the actuation was experimentally evaluated.

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 score0.002

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 head0.005
GPT teacher head0.240
Teacher spread0.236 · 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

Citations6
Published2009
Admission routes2
Has abstractyes

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