Supervisory control of flexible-manufacturing workcells that allow the production of a priori unplanned part types
Bibliographic record
Abstract
Traditionally, the function of a workcell supervisor has been to coordinate the various elements comprising the workcell for the production of a specific a-priori set of parts using a predetermined set of machines. Correspondingly, current workcell supervisor synthesis methodologies assume that the part production mix remains constant. In practice, however, the product mix may change over time. New part types are required to be processed, while other (nominal) part types may be temporarily discontinued. The workcell supervisory control approach proposed in this paper is that of utilizing a pair of non-communicating independent supervisors, working in concert to achieve the production of nominal and a-priori unplanned-for new part types: nominal and complementary supervisors. The nominal supervisor is responsible for controlling the behavior of the nominal workcell, producing the set of a-priori planned part types, while the complementary supervisor controls the flow of the unplanned-for new part types, where each supervisor is synthesized individually.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".