Neural Network Signal Processing in Spark Assisted Chemical Engraving (SACE) Micromachining
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Spark Assisted Chemical Engraving (SACE) is an emerging micromanufacturing technology of mainly non-conductive materials like glass and ceramic. The micromachining happens due to high temperature etching in electrolytic solution by electrochemical discharges which are generated through a tool-electrode across a gas film surrounding it. The gas film shall be present so that discharges, which are the heat source, can be generated hence causing local machining of the substrate. Studies have shown that the gas film breaks and reforms every few milliseconds depending on several factors, some of which are not known or are unclearly understood. Investigation of the gas film formation, its characteristics and the factors that affect its stability could lead to enhancing the SACE machining performance. In this work an algorithm based on Artificial Neural Networks (ANN) is developed to accurately estimate the gas film formation time. The method shown is a comprehensive one that can be applied to various machining conditions of the SACE process. To our best knowledge, few attempts have been done in the field of SACE signal processing and this work is the first study where ANN is used for gas film parameters calculation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it