Alternate Environmentally Friendly De-Painting Process for Aircraft Structures-Atmospheric Plasma
Bibliographic record
Abstract
During the lifetime of aircraft structures, paint removal and re-application cycles are conducted to restore appearance and to enable inspection for fatigue cracks and corrosion damages. Current paint removals processes, including chemical and abrasive media blasting, yield large amounts of volatile organic compounds and hazardous air pollutants, and generate large quantities of waste which require proper disposal/treatment. They also have the potential to mask surface cracks and decrease the effectiveness of Liquid Penetrant Inspections (LPI). This study compares current paint stripping methods to a novel Atmospheric Plasma (AP) system, which claims to be a more environmentally friendly method. Aluminium and steel coupons were prepared with military aircraft quality topcoat and primer paint schemes and subjected to the three aforemen-tioned paint stripping processes. The results were then characterized in terms of paint removal effectiveness, potential damages to the metallic substrates, and effect on the LPI process. Hardness and conductivity measurements as well as metallographic sectioning and microscopy, were used to characterize the stripped samples. The results indicated that AP had no negative ramifications on detecting fatigue cracks in the substrates, and did not alter the temper, mechanical properties of the aerospace alloys studied. The AP process has the potential to replace current hazardous and less environmentally friendly paint removal methods; though a full systematic qualification and evaluation process is still required for it to be considered as an accepted industrial paint removal process.
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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 | 0.000 |
| 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".