Developing a supervised machine‐learning model capable of distinguishing fiber orientation of polymer composite samples nondestructively tested using active ultrasonics
Bibliographic record
Abstract
Abstract This study evaluated the paired performance of different signal processing techniques and supervised learning models being capable of identifying subtle differences in otherwise similar acoustic signals related to detecting the fiber orientation of a polymer composite. Projection of Latent Structures models demonstrated poor predictive capabilities of the composite structure based on spectral analysis of the acoustic signal. AI based models showed great improvements to the capabilities, with artificial neural network modeling exceeding Convolutional Neural Networks for correct classification accuracies. The continuous wavelet transfer highlighted the greatest degree of differences in the signal response compared with fast Fourier Transformation or short time Fourier transformation. The use of regression‐based predictions over classification‐based was found to greatly improve the predictive capabilities of the models, especially when multiple fiber orientations were present in a sample. A time‐based analysis of spectral data showed the frequencies of the signal changed based on the orientation of the fibers. The acoustic signals for the samples with multiple fiber orientations contained individual artifacts representing components of each individual orientation. Use of the frequency domain was shown as capable of observing the targeted fiber information within the bulk material in real‐time. This work shows great promise for composite material predictions using active ultrasonics, with the potential to be implemented into in‐line systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".