Model-Based Virtual Sensors and Core-Temperature Observers in Thermoforming Applications
Bibliographic record
Abstract
In this paper, we present the notion of model-based virtual sensors (MBVSs) and virtual core-temperature observers in a thermoforming process. The concept of MBVSs allows for additional surface-temperature measurement points in addition to the already existing infrared sensors. This leads to improved controllability of the plastic sheet temperature and increased accuracy in temperature zoning, thus eliminating the use of extra infrared sensors, which significantly reduces the cost of the control system. The problem of core sheet temperature measurement is also addressed through the application of a closed-loop Luenberger core-temperature observer to estimate the center-plane temperature of the plastic sheet since it is not practical to have any kind of actual core-temperature measurement during the heating process. The two concepts of virtual sensors and virtual core-temperature observers are then combined to form an overall observer-based closed-loop control system. Finally, the functionality, performance, and robustness of the new system is investigated through simulation of an industrial-type thermoforming machine.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".