Towards controlling the surface texture of machined features during sparkassisted chemical engraving (SACE)
Bibliographic record
Abstract
Controlling the texture of micro-channels is vital for a plenty of applications, including solar cells, biomineralization, and scaffolds for growing cells. Furthermore, the significance of textured micro-channels on glass devices is purported to play a vital role in lab-on-chip devices and biomedical applications. Spark Assisted Chemical Engraving is a novel micromachining method capable of machining micro-channels on glass and ceramic devices while simultaneously texturing the surface. It was previously reported that, among other factors, the electrolyte concentration had the highest effect on the surface texture, where textures ranged from feathery-like to porous spongy-like as the concentration increased. Other factors included the tool speed and pulseoff time. An experimental setup has been designed and built to manufacture precision micro-channels using SACE technology and investigate the effects of different parameters on the surface texture. These parameters include the current and voltage signals, the electrolyte concentration and viscosity, the tool rotational speed, and the gas film characteristics, including gas film formation time, lifetime, and thickness. In this paper, the designed SACE setup is used to machine a range of microchannels with varying depths and investigate the effect of different AC voltage signals on the texture of the machined surface using different electrolyte concentrations. Furthermore, a correlation between the electrolyte concentration and electrolyte viscosity will be established that should help in better controlling the surface texture of machined channels.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".