Corrosion Protection of ZK60 Wrought Magnesium Alloys by Micro-Arc Oxidation
Bibliographic record
Abstract
The influences of the forging process and micro-arc oxidation (MAO) coating on the corrosion behavior of ZK60 wrought magnesium alloys exposed to salt spray and constant stress corrosion conditions were investigated. The microstructure of the ZK60 Mg alloy specimens forged under different temperatures (i.e., 250, 300, and 450 °C) was characterized using metallography, EBSD, and SEM. It was demonstrated that the ZK60 alloy forged at 300 °C (i.e., ZK60EF-300) had finer grain and uniformly distributed β-phase and, thus, better corrosion resistance than the ZK60 forged at 450 °C. At the lower forging temperature (250 °C) twins formed in the ZK60 alloy, which accelerated the corrosion of the ZK60E-250 specimen. The MAO coating provided robust corrosion protection for all the ZK60 wrought Mg alloy substrates. The salt spray corrosion test results showed that when the MAO coating broke down at certain weak sites, the corrosion performance of the coated Mg alloy was predominantly determined by the alloy substrate. The stress corrosion behaviors of the uncoated and MAO-coated ZK60 alloy specimens were also investigated under a constant load of 80 MPa in 3.5 wt.% NaCl solution. The MAO coating was found to improve the stress-corrosion resistance of the ZK60 alloy pronouncedly.
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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".