Data-driven thermal modeling of in-situ Automated Fiber Placement
Bibliographic record
Abstract
This study proposes a data-driven model for thermal history prediction during in-situ Automated Fiber Placement of thermoplastic composites. Temperature data was experimentally collected with fast-response thermocouples placed within carbon fiber AS4/PEEK composite substrates. The temperature for various combinations of hot gas torch temperatures, heat source velocity, and locations through the thickness and width were collected. A feedforward neural network (FNN) was developed to predict the entire 3-dimensional thermal history. The FNN had five input features and one output: the temperature at a given position and a combination of the process parameters. The FNN predictions for data unseen during training are validated for cases of interpolation and extrapolation. As expected, suitable performance was obtained for cases of interpolation, but predictions suffered in extrapolation. The computational efficiency of FNNs makes them an appropriate candidate for on-line thermal history prediction or process optimization, given that they are used within their training range.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".