Guided wave scattering by a geometrical or damage feature: application to fatigue crack and machined notch
Bibliographic record
Abstract
Guided-wave based Non-Destructive Evaluation (NDE) and Structural Health Monitoring (SHM) systems validation under realistic conditions or environment requires complex setups. Numerical or theoretical approaches are useful to save time and cost associated with experimental tests but the interaction with realistic geometrical (rivets, thickness changes, stiffeners, extrusions) or damage features (fatigue cracks, fillet cracks, delaminations, disbonds) must be accurately captured in order to be representative. In this paper, an experimental methodology is presented for estimating the far-field scattering of geometrical or damage features. The principle is based on the use of a Hankel transform of the measured 3D velocity field in order to evaluate with precision the scattered pattern using a spatially averaged method. Application to scattering of a hole with simulated machined and real fatigue cracks is proposed. It is observed that the simulated machined crack generally used as a reference standard can only model accurately the transmission behaviour while the scattering patterns are only similar when the wavelength is about the size of the crack, limiting the practical use of machined cracks for experimental validation of SHM or NDE 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".