Hyperparameters Effect in Deep Convolutional Neural Network Model on Prediction of Fiber Orientation Distribution in Prepreg Platelet Molded Composites
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Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-0103.vid A framework is developed for the prediction of the meso-structure of prepreg platelet molded composites (PPMC) via machine learning. Finite element modeling is used to model different morphology PPMC plates subjected to a change in temperature. From these finite element models, surface strain data and local through thickness fiber orientation distribution tensor (a_ij) terms are extracted. A U-Net architecture deep convolutional neural network is trained to predict the local through the thickness a_ij terms from virtual PPMC plate surface strain data. The predictive capability is evaluated for U-Nets trained with different hyperparameters and compared to predictions made by a simpler artificial neural network.
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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 | 0.000 | 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