Electroplating Line Flexible Control using P-Time Petri Nets Modeling and Hoist Waiting Times Calculation
Why this work is in the frame
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Bibliographic record
Abstract
In automated electroplating lines, product quality depends on soak times in chemical tanks while line throughput depends on hoist moves cycle time. These parameters are antagonistic since on-line tuning of cycle time interferes with processing duration and thus quality, and vice versa. Furthermore, on-line tuning actions performed without exploiting process flexibility may affect hoist moves schedule feasibility and call for complex scheduling at the on-line level. In this paper a flexible control for electroplating lines (EPL) is proposed that allows quality and throughput tuning within calculated margins and with no need for hoist moves rescheduling. Firstly a P-time Petri Nets (P-time PNs) tool is used to model hoist move sequence. Afterwards, linear programs (LP) are proposed to determine cycle time and soak times tuning margins without the need to reschedule hoist moves. Flexibility will be achieved using empty-hoist wait times.
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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