<title>Fusion of visual and eddy current inspection results for the evaluation of corrosion damage in aircraft lap joints</title>
Bibliographic record
Abstract
The fusion of data from Edge of Light(EOL) and eddy current inspections of aircraft lap joints is investigated in this study. The pillowing deformation caused by corrosion products is estimated by the EOL technique first. Eddy current (ET) techniques, e.g. multi-frequency eddy current testing (MF-ET) and pulsed eddy current testing (P-ET), can provide depth-sensitive inspections of fuselage joints. The objective of this study is to investigate how the testing results obtained from the two different methods correlate to each other and what kind of complementary information is available in each result. This work contains two steps. First, the EOL inspection is quantified through a calibration process where a laser displacement sensor is used to provide the reference. The EOL estimation is for the total material loss while the eddy current or pulsed eddy current testing is employed to provide the complementary information on the remaining thickness. Second, the ET data are fused with the principle component analysis method and the results are calibrated by a calibration experiment. Finally, the bottom layer corrosion is estimated through the subtraction of EOL and ET results. The preliminary results are presented in this paper.
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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 | 0.001 | 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 | 0.000 | 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".