Data-Driven Control of Rotational Molding Process
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a data-driven modeling and control formulation for achieving a desired product quality in a uni-axial rotational molding process. To this end, a data driven state-space model of the process is first identified using experimental data. For a given trajectory of input moves (heater and cold air profiles), this dynamic model is able to predict the evolution of the measured variable (internal product temperature). The dynamic model is augmented with a quality model, which, relates the terminal predictions from the dynamic model to the quality variables (sinkhole area, ultrasonic spectra amplitude, impact test metric and viscosity). The dynamic and quality model are in turn utilized within a model predictive control (MPC) framework to achieve tight quality control for new batches. Experimental results demonstrate the utility of the MPC in achieving improved and tight quality control.
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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) | 0.001 | 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 it