Experimental assessment of guided waves far-field scattering around damage in metallic and composite structures
Bibliographic record
Abstract
Experimental damage simulation is useful for designing ultrasonic guided-wave based systems for non-destructive evaluation (NDE) and structural health monitoring (SHM). However, simulating the scattering of guided waves with geometrical (rivets, thickness changes, stiffeners, and extrusions) or damage features (fatigue cracks, fillet cracks, delaminations, and disbonds) remains a challenge. The objective of this work is to assess to which extent the interaction of ultrasonic guided waves with typical damage can be captured with an experimental model for a metallic structure and a composite structure. For the metallic structure, real fatigue cracks around a rivet hole are simulated by machined notches, while, for the composite structure, the impact damage is simulated by a single artificial delamination introduced into the laminate using two circular Teflon tapes during manufacturing. This paper implements an experimental methodology for estimating the far-field scattering for both simulated and real damage. Two co-localized rectangular piezoceramics are used to generate the guided waves and non-contact measurement is performed using a three-dimensional laser Doppler vibrometer (3D-LDV) to extract the required information for evaluation of the reflection, transmission, as well as the scattering behavior of the waves. The corresponding coefficients as a function of frequency, incident angle, and type of damage are extracted.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".