Detection of Surface Contamination During a Scheduled Inspection and Repair Investigation
Bibliographic record
Abstract
Abstract The Department of National Defence (DND) in Canada has implemented a revised maintenance program for aircraft weapon systems whose goal is to ensure that essential maintenance is accomplished and to assure the continued airworthiness of aeronautical products during in-service operation. The program especially affects legacy aircraft. A case study is being presented where an adhesion failure was detected during a scheduled inspection. After carrying out numerous wet adhesion tests which failed, followed by water break tests on the failed areas, surface contamination prior to organic coating application was suspected. Typical laboratory techniques could not detect any contamination. Use of state-of-the-art Time of Flight – Secondary Ion Mass Spectrometry (ToF-SIMS) with primary ion source, Bi3+, was needed to determine the presence of surface contamination as low as a monolayer. The contamination was positively identified as a cationic surfactant that is commonly used in aircraft cleaners and conversion coating solutions. Further analyses using Scanning Electron Microscopy – Energy Dispersive X-ray Analyses (SEM-EDX), Thermal Gravimetric Analyses (TGA), Differenctial Scanning Calorimetry (DSC) and Dynamic Mechanical Analyses (DMA) were used to ensure the bulk integrity of the failed coatings.
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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 | 0.001 | 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".