Bolt hole eddy current testing probability of detection Part II: numerical modeling as a cost-reduction tool
Bibliographic record
Abstract
Probability of detection (PoD) studies are broadly used to provide data for damage tolerance life estimations and to determine the reliability of specific nondestructive inspection procedures. They require inspections on a large number of samples or components, a fact that makes these statistical assessments time and cost consuming. Numerical simulations could be used as a cost-effective alternative to empirical investigations for predicting the inspection outputs as a function of the inspection characteristics. The present paper focuses on the modeling aspects of eddy current testing and represents a sequel of the work formerly discussed in Part I and referring to 'Experimental Design and Data Analysis' for the bolt-hole inspection of wing box aircraft structures. A boundary-element numerical modeling software was employed to predict the eddy current signal responses when changing inspection variables related to probe, flaw, and material properties. A demonstrator exercise was used for inspection predictions in the case of lowering the eddy current testing frequency and inclusion of the simulated data in the PoD analysis. It was found that the numerical simulations have the potential to partially substitute or complement experimental data required for PoD studies, reducing the cost, time and resources required for a full experimental PoD assessment.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".