Inverse characterization of adhesive shear modulus in bonded stiffeners using ultrasonic guided waves
Bibliographic record
Abstract
Adhesively bonded stiffeners are widely used in aerospace structures. A well-cured bond can increase structural stiffness, and, consequently, enhance the performance of the entire structure. The feasibility of feature-guided wave modes for the inspection of the bond line adhesion in difficult-to-access regions has been already investigated in the literature. However, due to the complexity of the guide wave phenomena in the bond line region, a more comprehensive methodology to identify the curing state remains an open issue. This work introduces a multi-mode and multi-frequency inverse method for the characterization of stiffener bonded line using Semi-Analytical Finite Element (SAFE). Experiments were conducted on a T-shaped stiffener bonded to an aluminum plate. The feature-guided modes were excited using a piezoelectric shear transducer and measured using a laser interferometer at several times along a period of four days. The experimental dispersion curves were computed from the measured data and then systematically compared to the theoretical solutions obtained with the SAFE model. At each measurement, the shear modulus of the adhesive material could be estimated by iteratively minimizing the error between the experimental and numerical data. The results showed an abrupt increase in the shear modulus from the first to the second day, suggesting that the end of the curing processes was achieved. In general, the inverse scheme presented in this work was shown to be very sensitive, being able to distinguish differences of 5% in the shear modulus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".