Thermal control design for an automated fiber placement machine
Bibliographic record
Abstract
Abstract One of the important concerns about the quality of the thermoplastic composite in automated fiber placement (AFP) process is the degradation of thermoplastic resin, resulting from the overheating or the lack of proper heating by the heating system used in the AFP machine head. Heat transfer between the heating system and incoming pre-impregnated tow is not easy to control and can result in energy loss or nonconsistent heating of pre-impregnated tow. Advanced control systems are used to control the key processing parameters of the nip point temperature of the heating system. In this study, two advanced control systems are designed by using the dynamic thermal model of the fiber placement process. One is linear quadratic regulator controller, which is implemented to achieve optimal results for quality performance. The other is model predictive controller, which is proved more efficient as the physical capacity, safety, and performance constraints of the heating system are explicitly addressed in the controller design. Polyether ether ketone reinforced with carbon fiber (APC-2) is used as the tow material in this study. The results of this study are presented including a comparison of the performance of the two control strategies through simulation study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".