M<sup>3</sup>EDM: MEMS-enabled micro-electro-discharge machining
Bibliographic record
Abstract
This paper reports a photolithography compatible micro-electro-discharge machining technique that is performed with microelectrode actuators driven by hydrodynamic force. The movable planar electrodes suspended by the anchors are microfabricated directly on the workpiece. The electrode structures with fixed–fixed and cantilever configurations are defined by patterning 18 µm thick copper foil laminated on the workpiece through an intermediate photoresist layer and released by sacrificial etching of the resist layer. All the patterning and sacrificial etching steps are performed using dry-film photoresists towards achieving high scalability of the machining technique to large-area applications. The parasitic capacitance of the electrode structure is used to form a resistance–capacitance circuit for the generation of pulsed spark discharge between the electrode and the workpiece. The suspended electrodes are actuated towards the workpiece using the downflow of dielectric machining fluid, initiating and sustaining the machining process. Micromachining of stainless steel is experimentally demonstrated with a machining voltage of 90 V and continuous flow of the fluid at a velocity of 3.4–3.9 m s−1, providing a removal depth of 20 µm with an average surface roughness of 520 nm. The experimental results of the electrode actuation are shown to agree well with the theoretical estimations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".