Modeling the Forces Exerted on the Tool-Electrode During Spark Assisted Chemical Engraving Constant Velocity Feed-Drilling
Bibliographic record
Abstract
Spark Assisted Chemical Engraving (SACE) is an interesting technology for micro-machining several types of non-conductive materials like glass, quartz, polymers and some ceramics. The process takes place in an electrochemical cell with two electrodes immersed in an electrolyte. The electrolytic solution is typically sodium hydroxide (30%wt NaOH) or potassium hydroxide (KOH). The cathode is used as tool and the anode as counter-electrode. When the applied voltage is higher than a critical value (typically around 30V, depending on the electrolyte and tool-electrode geometry) bubbles grow so dense on the electrode surface that they coalesce into a gas film. Electrical discharges occur between the electrode and the electrolyte. Machining begins consequently if the electrode is placed close enough to the surface to be machined (typically 25μm for glass). In the present paper, the forces exerted on the tool-electrode during constant velocity feed drilling is investigated experimentally and a model is proposed. The setup is composed of a machine head mounted on XYZ precision linear stage holding the tool-electrode. The machining head further incorporates a force sensor which is able to monitor, during drilling operation, the force exerted on the tool-electrode based on the zero displacement measurement principle.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".