Pulsed Eddy Current Inspections of Aircraft Structures in Support of Holistic Damage Tolerance
Bibliographic record
Abstract
Riveted fuselage splice joints are a common feature in the construction of transport aircraft. Traditional durability and damage tolerance analyses of these joints have often ignored or greatly simplified the effect of corrosion damage and its interaction with fatigue. This has required that corrosion damage be repaired as soon as it is detected, which has in turn discouraged the use of sensitive nondestructive inspection (NDI) techniques which may find structurally insignificant amounts of damage. New holistic life assessment models which do account for corrosion damage are under development by many research groups including the National Research Council Canada. These models require quantitative assessment of corrosion damage as well as fatigue damage. Pulsed eddy current (PEC) inspection methods have been developed to address these needs, and this paper presents the results of a signal processing technique developed to characterize material loss in a two-layer structure from PEC data. The goal of the technique is to map the thickness of both the 1st and 2nd layers. Applying this method to PEC data measured on a laboratory test specimen shows that corrosion can be quantified with an error of less than 4% of a layer thickness. The effect of NDI error in corrosion quantification on maintenance and repair is estimated using a holistic, probabilistic life assessment model. The paper will discuss how these models, along with verified NDI techniques, can be used to implement new proactive maintenance paradigms for aircraft structural components.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".