Low-Frequency Eddy-Current Testing for Detection of Subsurface Cracks in CF-188 Stub Flange
Bibliographic record
Abstract
The vertical stub flanges on the CF-188 Hornet fighter aircraft are responsible for linking two vertical stabilizers to the fuselage. Repeated stresses due to dynamic loads on aircraft structure during flight may cause eyebrow cracks on the flange around fastener holes. Prevention of failure of the flange structure involves early detection before cracks grow to a critical length. Low-frequency eddy-current (LFEC) techniques have been applied to inspect thick conducting aircraft structures. However, in the case of the stub flange, LFEC is challenged by component geometry. In particular, the surface containing cracks is not parallel to the surface that is accessible for scanning. The bolts are perpendicular to the face with cracks but are almost at 85° to the scanning surface. A novel conical probe is designed to use the bolt as a core for the excitation (driver) coil, thereby increasing driving flux density, and to constrain probe positioning as it is swept around the bolt. Finite-element simulations are used to investigate influence of different parameters on LFEC impedance plane response. These include slope of the slanted surface, sample thickness, operating frequency, crack size, and edge effect for two different component edge shapes. Experimental measurements carried out at different frequencies on test samples, prepared with the same dimensions as actual flanges, were found to be in good agreement with computational models. Results indicate that LFEC is significantly affected by surrounding geometries, which therefore, need to be taken into account when inspecting for cracks.
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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 | 0.000 | 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".