Laplacian wavenumber filtering for improving damage visualization in fast non-contact inspections
Bibliographic record
Abstract
Full acoustic wavefield data acquired over large areas may provide a unique insight about the presence of defects in the monitoring of shells and plates. However, full wavefield imaging techniques have some limitations, including slow data acquisition and lack of accuracy. This research addresses both of these challenges and presents a fast and robust non-contact wavefield imaging method based on the Compressive Sensing (CS) approach, as a mean to speed up the acquisition process, and a novel analysis tool to process recovered wavefield data in the wavenumber/ frequency domain. The proposed strategy is based on the removal of the injected wave from the overall response, in order to highlight the presence of reflections associated with damage. This strategy is based on the application of the (3DFT) to the CS reconstructed wavefields to produce the frequency wavenumber representation. The frequency-wavenumber coefficients are then thresholded, and, finally, a Laplacian filter is applied to enhance the discontinuities. This concept was tested over multiple experiments with different panels. Tests were performed on aluminum and composite plates, and the defect was simulated with an attached mass. The results demonstrate the capability of the technique for enhancing damage visualization while reducing the original number of scan points.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".