Development of a Computationally Efficient Model of the Heating Phase in Thermoforming Process Based on the Experimental Radiation Pattern of Heaters
Bibliographic record
Abstract
In this study, an accurate and computationally efficient model for the heating process of thin thermoplastic sheets during thermoforming is developed. This model opens the door to efficient training of model-free control approaches in thermoforming applications, which often require extensive training data that would be significantly costly and time-consuming to generate using physical setups. This model takes into account heat transfer via radiation between heaters and the sheet, heat transfer via conduction through the sheet, and heat transfer via convection between the sheet and the ambient. In this paper, rather than using an analytical relationship for the view factor, an experiment is designed to determine the exact radiation pattern of the heater on the sheet and the fraction of infrared emission absorbed by the sheet. Comparing the output temperature profile on the sheet from the designed model to IR images from a laboratory-scale heating system indicates that the mean square error is reduced by around four times when compared to traditional models with analytical view factors. Moreover, a comparison of the computation time with COMSOL software for a scenario with the same configuration of computation hardware reveals that the designed model is almost ten times faster.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".