Improved damage imaging in aerospace structures using a piezoceramic hybrid pin-force wave generation model
Bibliographic record
Abstract
In this study, a correlation-based imaging technique called "Excitelet" is used to monitor an aerospace grade aluminum plate, representative of an aircraft component. The principle is based on ultrasonic guided wave generation and sensing using three piezoceramic (PZT) transducers, and measurement of reflections induced by potential defects. The method uses a propagation model to correlate measured signals with a bank of signals and imaging is performed using a roundrobin procedure (Full-Matrix Capture). The formulation compares two models for the complex transducer dynamics: one where the shear stress at the tip of the PZT is considered to vary as a function of the frequency generated, and one where the PZT is discretized in order to consider the shear distribution under the PZT. This method allows taking into account the transducer dynamics and finite dimensions, multi-modal and dispersive characteristics of the material and complex interactions between guided wave and damages. Experimental validation has been conducted on an aerospace grade aluminum joint instrumented with three circular PZTs of 10 mm diameter. A magnet, acting as a reflector, is used in order to simulate a local reflection in the structure. It is demonstrated that the defect can be accurately detected and localized. The two models proposed are compared to the classical pin-force model, using narrow and broad-band excitations. The results demonstrate the potential of the proposed imaging techniques for damage monitoring of aerospace structures considering improved models for guided wave generation and propagation.
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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 | 0.001 | 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".