Numerical modelling as a cost-reduction tool for probability of detection of bolt hole eddy current testing
Why this work is in the frame
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Bibliographic record
Abstract
Probability of detection (PoD) studies are broadly used to determine the reliability of specific nondestructive inspection procedures, as well as to provide data for damage tolerance life estimations and calculation of inspection intervals for critical components. They require inspections on a large set of samples, a fact that makes these statistical assessments time- and cost-consuming. Physics-based numerical simulations of nondestructive testing inspections could be used as a cost-effective alternative to empirical investigations. They realistically predict the inspection outputs as functions of the input characteristics related to the test piece, transducer and instrument settings, which are subsequently used to partially substitute and/or complement inspection data in PoD analysis. This work focuses on the numerical modelling aspects of eddy current testing for the bolt hole inspections of wing box structures typical of the Lockheed Martin C-130 Hercules and P-3 Orion aircraft, found in the air force inventory of many countries. Boundary element-based numerical modelling software was employed to predict the eddy current signal responses when varying inspection parameters related to probe characteristics, crack geometry and test piece properties. Two demonstrator exercises were used for eddy current signal prediction when lowering the driver probe frequency and changing the material's electrical conductivity, followed by subsequent discussions and examination of the implications on using simulated data in the PoD analysis. Despite some simplifying assumptions, the modelled eddy current signals were found to provide similar results to the actual inspections. It is concluded that physics-based numerical simulations have the potential to partially substitute or complement inspection data required for PoD studies, reducing the cost, time, effort and resources necessary for a full empirical PoD assessment.
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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 | 0.002 | 0.005 |
| 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 it