Magnesium Alloy Effects on Plasma Electrolytic Oxidation Electro-Ceramic and Electro-Coat Formation and Corrosion Resistance
Bibliographic record
Abstract
Plasma electrolytic oxidation (PEO) electro-ceramic (EC) coatings with and without an epoxy-base electro-coat (E-coat) overlayer were studied on four magnesium alloys using the same process deposition parameters: Mg-3Al-1Zn (AZ31B), Mg-9Al-0.5Zn (AZ91D), Mg-1.5Zn-0.3Zr-<0.5Nd (ZE10A, ZEK100 type), and Mg-4Y-(<2.5 Nd)-0.4Zr (WE43). A similar magnesium-fluoride (Mg-F) base EC chemistry formed on all four alloys. However, the morphology of the EC was significantly impacted by the substrate alloys, with higher alloy Al or Zr/rare earth (RE) levels also resulting in thicker coatings. A duplex EC structure formed on AZ31B, AZ91D, and ZE10, with a submicron inner layer and a thicker outer porous layer; whereas a striated multi-layer inner region and thick porous outer region formed on WE43. The EC coated alloys exhibited similar corrosion resistance to the bare alloys due to coating porosity, with the best corrosion resistance exhibited by AZ91D and WE43. After applying the E-coat overlayer, similar, excellent corrosion resistance was exhibited for all four EC + E-coated alloys. Scribing of the EC + E-coat to controllably introduce a coating defect degraded corrosion resistance, particularly for WE43, which suffered from E-coat blistering due to susceptibility of the inner striated EC layer to delamination.
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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".