A Multitasking Environment for Real-Time Monitoring of Discharging Activity During SACE Process Using LSTM
Why this work is in the frame
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Bibliographic record
Abstract
Real-time control of SACE gas film stability is crucial, as it significantly impacts micromachining repeata-bility and quality in this technology. Gas film stability and discharging activity are interconnected, and monitoring real-time parameters like mean discharge current and energy, which serve as indicators of gas film stability, is the first step in this effort. An intelligent algorithm deployed on a dSPACE platform uses LSTM for online discharge activity monitoring, identifying discharges and calculating indicators. Maintaining a short enough sampling time for prompt discharge detection presents overrun errors. Therefore, a real-time multitasking environment with a 1.6e-5 seconds sample time is executed. A more complex LSTM enhances detection accuracy but ex-tends execution time, potentially resulting in more unprocessed data loss. The research examines the real-time model with various algorithm feed batch sizes and LSTM complexities, particularly the number of hidden units. An example of a 2-hidden-unit LSTM demonstrates promising 90.45% accuracy, processing data every 264 milliseconds with a 131-millisecond batch (approximately 0.5 processing ratio), indicating superior performance. In the future, exploring LSTM hyperparameter optimization and real-time model parameter tuning is recommended to enhance accuracy and processing ratio.
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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