A model predictive controller of plastic sheet temperature for a thermoforming process
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a method to control the surface temperature of a plastic sheet using model predictive control (MPC). Although control techniques have been developed for the heating phase of the thermoforming process, oven heater temperatures in the thermoforming industry are still largely adjusted by trial and error based on the experience of the operator. MPC is one of the advanced methods for process control that has been used in different plants since the 1980s. Even though the MPC controller can handle a multivariable process, the large number of computations makes it difficult to apply to large systems such as multi-zone temperature control in a thermoforming machine. In this paper, the design of a model predictive controller is reported and implemented on a complex thermoforming oven with a large number of inputs and outputs for precise control of sheet temperatures under hard constraints on heater temperature and their rates.
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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