Electroplating Line Flexible Control using P-Time Petri Nets Modeling and Hoist Waiting Times Calculation
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".