STFT-CNN enabled quantitative detection of liquid ingress in honeycomb composites via infrared thermography
Bibliographic record
Abstract
Honeycomb sandwich composites are widely used in various industries due to their exceptional properties, but they face the challenge of liquid infiltration due to their inherent hollow structure. This study proposes a novel method for quantifying liquid volume in honeycomb structures using pulsed thermography and a deep learning network. By combining the powerful image feature extraction capabilities of convolutional neural networks (CNNs) and the time-frequency analysis advantages of the short-time Fourier transform (STFT), the network effectively extracts spatiotemporal features from the data. Finite element simulation, theoretical analysis, and experimental validation demonstrate the effectiveness of the proposed method. In the near-liquid configuration, the proposed STFT-CNN model can predict liquid volumes up to about 22.5% of the full capacity. For far-liquid configuration, the model demonstrates excellent performance in predicting a wide range of liquid volumes, from very small amounts to near-full capacity of a honeycomb core. Our proposed method provides a valuable tool for monitoring the health and integrity of honeycomb structures.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".