The influence of fabric architecture on impregnation behavior and void formation: Artificial neural network and statistical‐based analysis
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This work proposes an approach combining artificial neural networks (ANN) with statistical models to predict injection processing conditions for four reinforcement architectures: plain weave, bidirectional noncrimp fabrics, unidirectional fabrics (Uni) and random fiber mats (Random). Key results allow evaluating the velocity of the flow front by combining processing parameters and creating a three‐dimensional response surface based on a properly trained ANN. This investigation is based on a large number of experimental results. The key role played by some physical parameters was associated with predicting the impregnation behavior (velocity of the flow front) during resin injection. The main outcome aims to provide a better control of void content in terms of size and position to the four fibrous reinforcements considered.
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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 | 0.000 |
| 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 it