The Effect of Atmospheric Plasma Paint Stripping on the Fatigue Crack Growth Properties of Aluminium Substrates
Why this work is in the frame
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Bibliographic record
Abstract
Paint removal is a common maintenance requirement for aircraft as well as naval and land vehicles, since external paint gets damaged and loses much of its corrosion protection effectiveness with time. Paint removal is also conducted when metallic aircraft structures are inspected periodically for fatigue cracks and corrosion. The conventional methods of removing paint employed throughout the Canadian Forces mainly include chemical stripping and abrasive media blasting. Chemical stripping involves the use of hazardous chemicals, which are high in Volatile Organic Compounds (VOC) and Hazardous Air Pollutants (HAP). Abrasive media blasting typically results in a substantial quantity of solid waste consisting of paint and blast residues. Such waste is subject to control under increasingly stringent environmental and safety regulations and its disposal is costly. The new Atmospheric Plasma (AP) paint removal process purports to be a high chemical energy, low thermal energy (cold plasma process), that should not damage temperature sensitive substructures, such as heat treated aerospace aluminium alloys. Fatigue strength is one of the key properties in aircraft structures. In order for AP paint stripping to be accepted as an aerospace industry standard paint removal process, it must be thoroughly tested to demonstrate that it does not adversely affect the fatigue properties of the substrate. This paper investigates effect of the paint removal process on fatigue crack growth of 7075-T6 and 2024-T3 aluminium panels.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it