Theory-guided machine learning for thermal modeling of in-situ automated fiber placement of thermoplastic composites
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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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".