Preparation and Properties of Cerium Conversion Coatings on the Surface of Aluminum Alloy LY12
Bibliographic record
Abstract
In order to improve the repairing quality of aircraft envelope of the aluminum alloy LY12， the cerium conversion coating technology was studied by using the surface modification technique. Principle experiment was on the basis of aviation repair technological requirement, refers to the related technique, through examination of corrosion resistance and wear resistance performance of cerium conversion coating, the principal composition of the formula for making cerium conversion coating:Ce(NO3)3 + KMnO4+ Ce-1 (chemical additive), In order to determine each composition of the formula and the technology parameters scientifically, L9(34) orthogonal testing method was adopted, and the formula of surface modification solution was optimized, the technological conditions for making cerium conversion coating on the surface of aircraft envelope were determined. Results of corrosion resistance and wear resistance of the cerium conversion coating on the surface of the aluminum alloy LY12 aircraft envelope obtained by the new technology showed: 1. On the given test conditions, the best content of each chemical composition in the formula of modification solution for making cerium conversion coating on the surface were: l.Ce(NO3)3：14g/L,KMnO4：1g/L,addictive Ce-1:0.3g/L；Main technology parameters were ：pH=1.5～2.7;temperature：20°C；time:15~25 min.；2. Under the same test condition, the corrosion resistance of surface of the cerium conversion coating obtained in test modification solution was better than Alodine 1001 obtained from Bombardier Corporation of Canada, and can meet the repairing demands of Aircraft Envelope.
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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".