Using Modeling and Metamodels for Reliability Study in Non-Destructive Evaluation
Bibliographic record
Abstract
As in any other measurement process, NDE is subject to variability whose impact can be assessed to guarantee a given level of performance. Once NDE prevents catastrophic failures, deaths and environmental damage, identifying uncertainties and variability in NDE help to design more reliable inspections, therefore is a process that saves lives. This is the goal of a reliability study. Statistical indicators such as Probability of Detection (POD) curves give insights to allow building of mechanical designs with enough « secure margin » for structural integrity and to also define appropriate maintenance & inspection cycles. Simulation is very useful to support performance or reliability demonstrations that require a lot of data (such as POD studies and qualification campaigns), and where simulation can help by reducing the number of necessary mock-ups and experimental trials. In addition to physical models, the NDE simulation software CIVA now offers meta-modelling techniques. Built from an initial set of physical simulations, such surrogate models give the user the possibility to generate a massive amount of data while combining and exploring multi parametric variations. This is particularly efficient in the context of reliability studies, when you have to find the best settings, track the worst-case scenario or build POD curves. This paper illustrates the use of this meta-modelling approach for the reliability study of a longitudinal weld AUT inspection. Real pipe mill inspection data are provided and compared to modelling and meta-modelling results.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".