Laser-ultrasonic detection of hidden corrosion in aircraft structure
Bibliographic record
Abstract
Corrosion has been recognized as a serious problem in the maintenance of aging aircraft. The Industrial Materials Institute (IMI) has explored the use of laser-ultrasonics for the detection of hidden corrosion in metallic lap joint structures. For inspection with painted surfaces, IMI has shown that a resonance spectroscopy approach using a simple two-layer model can be used to determine the thickness of the paint layer and of the top metal skin. Validation of the model has been made using a test sample with a broad range of paint thickness. Once combined with a numerical inversion method, the model is used to produce a thickness map of the top metal skin from measured resonance frequencies. Results from standard samples with flat-bottom holes showed that the laser-ultrasonic technique could detect metal loss below 1%. The reliability of the method was also demonstrated on accelerated corrosion samples. Comparison to X-ray images showed that the laser-ultrasonic method presented a thickness map that had the same accuracy as the X-ray system without the need for dismantling the sample. These results indicated that laser-ultrasonics could be a useful tool not only to inspect aircraft during routine maintenance but also to provide valuable data in the study of corrosion inception and growth in lap joint structures.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".