Optimal selection of non-destructive inspection technique for welded components
Bibliographic record
Abstract
The objective of this paper is to select the optimum non-destructive inspection (NDI) technique and its associated in-service inspection interval for welded components subjected to cyclic loading fatigue. The objective function (total cost) is formulated as a function of the decision variables (controllable) and the condition of the inspected asset (uncontrollable). Total cost consists of inspection cost and repair cost over the lifetime of the asset in addition to the cost of unexpected failure as a result of failure to detect a growing crack before reaching a critical size. The decision variables are the reliability of the NDI technique and the inspection interval. Two main parameters are used to quantify the reliability of NDI techniques. These are the Probability of Detection function (POD) and the Probability of False Calls (PFC). The condition of the inspected asset is represented by the probability of existence of a crack in the asset at the inspection time and the expected critical time to failure. The objective function is minimised for different NDI techniques subject to a safety constraint that the probability of failure does not exceed a predefined level. The NDI with the lowest objective function level is chosen as the preferred solution.
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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 | 0.003 |
| 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 | 0.000 |
| 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".