Simulation-Based Inversion for the Characterization of Adhesively Bonded Joints Using Ultrasonic Guided Waves
Bibliographic record
Abstract
Adhesively bonded structures are widely used to facilitate the manufacturing process and enhance the performance of critical components in the aerospace, automotive, and energy industries. The assessment of the bond layer using the propagation of ultrasonic guided waves has been extensively investigated in the literature using several different approaches. In this study, a finite element (FE) model was used to simulate the dispersion curves of the modes propagating in an aluminum/adhesive/aluminum bonded structure. The simulated dispersion curves were systematically compared with the experimental measurements to retrieve the shear modulus of the adhesive layer during its curing process. The optimization procedure was able to perform inversion with minimum prior knowledge of the adhesive layer properties. In general, the proposed FE-based forward model was able to match the experimental dispersion curves during curing. Notwithstanding some discrepancies observed in the early to intermediate state of curing, the predicted model parameters were in agreement within 6% of the values obtained by the reference methods. The optimal shear modulus was estimated at 1.55 GPa at the end of the curing, against a reference value of 1.47 GPa.
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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 | 0.000 |
| 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".