Effect of Machining Limiting Factors on Drilling Progress during Spark Assisted Chemical Engraving (SACE): General Trends
Bibliographic record
Abstract
Spark Assisted Chemical Engraving (SACE) is a micro-machining technology for non-conductive materials, mainly glass, based on thermal assisted etching. Generally, during SACE, drilling proceeds at a fast rate reaching 100 µm/s for the first 100 µm and then it slows down for depths higher than 300 µm. While several techniques have been proposed to establish faster drilling, they mainly rely on tuning the machining parameters to enhance the machining performance. However, with this approach machining parameters need to be constantly tuned to achieve certain machining performance depending on the size of the tool and the features needed. Therefore, this necessitates further work to enhance understanding regarding the SACE machining process fundamentals in order to enhance machining speed and quality. Since SACE is a thermal assisted etching process, both local heating and flushing of electrolyte in the machining zone are required. However, to the authors’ knowledge there is not any study that attempts to analyze the effect of each of these machining limiting factors on the machining performance. This work attempts to clarify the effect of each flushing and heating on the drilling progress for hole depths higher than 100 microns. It therefore provides a deeper understanding of the fundamentals of the SACE machining process.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".