Theory-guided machine learning for thermal modeling of in-situ automated fiber placement of thermoplastic composites
Why this work is in the frame
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Bibliographic record
Abstract
In-situ Automated Fiber Placement (AFP) of thermoplastic composites has several advantages over traditional manufacturing techniques, with the main benefit being eliminating secondary thermal processing. Without secondary heat treatment, the in-situ thermal history becomes the critical process parameter that governs bond development, crystallization kinetics, and the development of residual stresses. This work improves the thermal modeling of the in-situ Automated Fiber Placement (AFP) manufacturing process by leveraging Theory-Guided Machine Learning (TGML). A novel theory-guided neural network (TgNN) with theory-based pre-layer transforms models the three-dimensional temperature distribution during in-situ AFP manufacturing. The TgNN is fit on experimentally measured temperatures for various combinations of hot gas torch temperatures and heat source velocities. Feature engineering is implemented by applying theory-based pre-layer transforms to the input features time, the thermocouple coordinates, hot gas torch temperature, and heat source velocity. Compared to a theory-agnostic neural network, the TgNN with theory-based pre-layer transforms has improved predictive ability and requires fewer training data for equivalent performance. The trained model is computationally efficient and can be leveraged for online process 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 | 0.001 | 0.001 |
| 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 it