Automata-based modeling and control synthesis for manufacturing workcells with part-routing flexibility
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
The utilization of flexible manufacturing workcells (FMCs) to produce families of parts in many possible orders of operations and choices of different machines is advantageous. The modeling and control of such discrete-event systems (DESs) have generally been performed in a hierarchical structure. Despite intensive research on the theoretical control of DESs, however, current techniques such as controlled automata can still primarily be used for the supervisory control of simple cells. In the paper, a modeling and control synthesis technique is presented for FMCs that allow part-routing flexibility. The proposed methodology combines extended Moore automata (EMA) and controlled-automata theories to synthesize supervisors for such FMCs. EMA-based supervisors have the capability to read (receive) multiple pieces of information regarding the behavior of the FMC. Based on this information they can explicitly generate control commands issued to cell devices. In order to significantly reduce the state-space of EMA-based supervisors without affecting the behavior enforced on the (controlled) FMC, a minimization procedure is also outlined.
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