On-line adaptive control for thermoforming of large thermoplastic sheet
Bibliographic record
Abstract
Large sheet thermoforming is widely used for the manufacture of parts such as twin sheet formed gas tanks, body panels and windshields. These complex technical parts require a precise temperature map to be realized prior to forming. In particular, the material used for forming gas tanks incorporates an EVOH barrier layer that is susceptible to tearing when exact processing conditions are not strictly respected. The problem is compounded by the fact that the temperature is presently controlled at the heating elements, while the temperature distribution across the thickness of the sheet is the main process variable. This makes the thermoforming process very susceptible to perturbations and greatly increases the number of rejected parts. In order to increase productivity and quality, the actual sheet temperature distribution before forming must adhere to the optimized temperature map as predicted by a process simulation or the recipe as determined by previous runs. The system presented here controls the amount of energy received by every sheet zone, which is equivalent to controlling the sheet temperature. It is tuned on-line by identifying the heating zone to sheet zone gains matrix using a flux meter and by correlating the matrix to the output of an infrared scanning thermometer located at the exit of the heating oven. The control system will realize the map of the required sheet surface temperature by adjusting the heating elements temperature. The system has been implemented on a Monark twin-sheet thermoforming machine that has dual 1.8mx1.8m square ovens and 504 heating elements in re-configurable zones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".