Qualification of Bolt-Hole Eddy Current Inspections Using Numerical and Experimental Input
Bibliographic record
Abstract
In the aerospace industry, reliability of non-destructive inspection (NDI) has significant economic and safety implications. It is commonly determined using expensive and extensive empirical probability of detection (POD) studies. Model-assisted qualification is increasingly used to establish the reliability of NDI systems. This approach has the potential to reduce time, costs and resources associated with fully empirical reliability studies, especially in the case where sufficient practical evidence is available. This work is a validation attempt of model-assisted qualification for bolt-hole eddy current inspections. It uses the length and depth of real fatigue cracks as characteristic input parameters in physics-based simulations, along with a number of uncertain variables, corresponding to inspection inconsistencies encountered in practice. Semi-analytical models and parametric studies are employed to replicate a large set of inspection conditions. The corresponding inspection outcomes are used to generate POD curves. Simulation-based POD outputs are compared with the ones obtained experimentally, during an earlier study.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".