Feasibility of high frequency guided wave crack monitoring
Bibliographic record
Abstract
Ultrasonic guided waves are particularly interesting for SHM applications because they have the ability to propagate long distances with minimal attenuation. Using the baseline subtraction approach, the signal from a defect free structure is subtracted from the actual monitoring signal to detect and characterize defects. Low frequency guided wave SHM and the interaction of the fundamental guided wave modes with various types of defect are well documented in the literature. There are, however, only a very limited number of studies on high order modes. High frequency guided waves may enable the detection of smaller cracks relative to conventional low frequency guided wave SHM. The main difficulty at high frequency is the existence of several modes with different velocities. This study investigates the scattering of high frequency Lamb waves around a through-thickness hole with a view to developing a highly sensitive SHM system for safety-critical components. A 3D finite element model of a 305 × 305 × 1.6 mm aluminum plate was used to determine the scattered field generated by cracks on the circumference of a through-thickness hole in the middle of the plate. Crack properties such as orientation, length and depth were studied. A subset of the finite element simulations were validated against experimental results. The experimental setup comprised a classic contact piezoelectric transducer bonded on the side of the plate and a laser interferometer detector.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".