Selective generation of ultrasonic guided waves in a bi-dimensional waveguide
Bibliographic record
Abstract
Non-destructive testing and structural health monitoring systems based on ultrasonic guided waves propagation are particularly used in civil engineering or aerospace applications. Guided waves are commonly employed as they propagate through large distances and can inspect the entire cross-section of the structure. In order to optimize the sensitivity to a specific damage type, it is often preferable to generate a carefully selected pure mode. Although single-mode generation has been achieved for Lamb waves in infinite plate-like structures, such generation is much harder in a rectangular bar since less conventional modes propagate in finite cross-section waveguides. This article presents a general methodology for mode selective generation in a finite cross-section waveguide, using multiple transducers. Obtaining modal identification through conventional spatial Fourier transform on a longitudinal scan has proven to be inconvenient for waveguides with a two-dimensional cross-section. An alternative technique is proposed, consisting in the decomposition over the modal basis of the three displacement components measured across the bar width at the bar surface. The methodology is applied to the single-mode generation within an aluminum bar instrumented with eight piezoelectric transducers bonded to the surface. The modal basis is obtained with a semi-analytical finite element method. Numerical simulations and experiments using a three-dimensional laser Doppler vibrometer are conducted in order to validate the methodology.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".