Adhesive bond testing by laser shock waves and laser interferometry
Bibliographic record
Abstract
Adhesive bonding of structural components presents many practical advantages when compared to other joining methods, but its application for critical components is limited by the absence of reliable nondestructive methods that can assure the bond strength of the joint. In this paper, a method based on shock waves produced by a pulsed laser is applied to the evaluation of bond strength of two plates joined with an adhesive. Different adhesives were tested. A shock wave, produced by an energetic short laser pulse can cause a delamination at the adhesive/plate interface when it propagates through them. A good bond is unaffected by a certain level of shock wave stress whereas a weaker or kissing bond is damaged. The method is made quantitative and in-situ by optically measuring the sample back surface velocity with a Doppler or velocity interferometer. The interferometer signals allow distinguishing interfaces that pass the test from the ones that fail. The measured back surface velocity is related to the internal stress by a simple equation. Experimental results show that the proposed test is able to differentiate bond quality and give a value of the bond strength. Laser-ultrasonic inspection made on laser shock tested samples confirms that weak bonds are revealed by the method. The proposed testing approach may help a broad adoption of adhesive bonding throughout the aerospace industries and its use for joining primary aircraft structures.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".