The Effect of Atmospheric Plasma Paint Stripping on the Fatigue Crack Growth Properties of Aluminium Substrates
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".