Wear and Galvanic Corrosion Protection of Mg alloy via Plasma Electrolytic Oxidation Process for Mg Engine Application
Bibliographic record
Abstract
Sliding wear of magnesium (Mg) engine cylinder bore surfaces and corrosion of Mg engine coolant channels are the two unsolved critical issues that automakers have to deal with in development of magnesium-intensive engines. In this paper, Plasma Electrolytic Oxidation (PEO) process was used to produce oxide coatings on AJ62 Mg alloy to provide wear and corrosion protection. In order to optimize the PEO process, orthogonal experiments were conducted to investigate the effect of PEO process parameters on the wear properties of PEO coatings. The PEO coatings showed a much better wear resistance, as well as a smaller friction coefficient, than the AJ62 substrate. The galvanic corrosion property of AJ62 Mg coupled with stainless steel and aluminum (Al) was investigated via immersion corrosion test in an engine coolant. Applying PEO coating on Mg can effectively prevent the galvanic corrosion attack to Mg. Due to the small thickness of the PEO coating, the thermal conductivity of Mg would not be appreciably affected. The test results showed a great promising in wear and corrosion protection of Mg for Mg engine application.
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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".