Laser ultrasonics in a multilayer structure: Plane wave synthesis and inverse problem for nondestructive evaluation of adhesive bondings
Bibliographic record
Abstract
A laser ultrasonic method is proposed for the nondestructive evaluation of bonded assemblies based on the analysis of elastic plane waves reflected from the bonding interface. Plane waves are numerically synthesized from experimentally detected cylindrical waves. Several angles of incidence with respect to the bonding interface are achieved by varying the delay in the synthesis step. An inverse problem using these plane waves is then solved to identify the normal and transverse interfacial stiffnesses that model the mechanical coupling between two bonded media. The semi-analytic model developed and detailed in Hodé et al. [J. Acoust. Soc. Am. 150, 2065 (2021)] is used to create the database that contains simulated laser-generated ultrasounds required to solve the inverse problem. The developed method is first validated with semi-analytic simulated input data where Gaussian noise has been added. Next, the method is applied using signals acquired on an aluminum alloy plate and on assemblies (with and without adhesion defects) made of two aluminum alloy plates bonded by an aeronautical structural epoxy adhesive film. Differences between the identified values of interfacial stiffnesses distinguish the three samples and obtain quantitative values to characterize the adhesive bonding.
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 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 | 0.001 | 0.001 |
| 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".