Corrosion Protection of Structural Magnesium Alloys: Recent Development
Bibliographic record
Abstract
Magnesium is one of the viable materials for significantly reducing the weight of automotive component. However, wide-scale use of magnesium alloy is complicated by a number performance factors related to the intrinsic properties of these alloys such as corrosion resistance. The latest developments in coating technology and alloy chemistry design have removed much of the barrier and a number of chromate-free processes are commercially available for the parts that require a coating or coatings. As part of the Structural Cast Magnesium Development (SCMD) project, several types of newly selected coatings were evaluated for protection against general and galvanic corrosion. The latest results are presented in this paper. Galvanic corrosion is a major concern for the parts that are joined with other metals, especially when there is salt-containing road poultice on the surface. While the use of compatible Al alloy isolators can significantly mitigate the problem, elimination of electrical contact or the electrochemical driving force between the dissimilar metals is essential for complete prevention of the problem. In the test in which a thin mylar film was inserted between a steel washer and the Mg surface, no visible galvanic corrosion was found after 40 cycles of GM9540P testing. Anodizing coating alone provides good resistance to general corrosion, but it does not seem be effective against galvanic corrosion. The latest experimental work suggests that application of coatings can also beneficially affect the resistance of Mg alloy to stress corrosion cracking (SCC). When treated with Alodine 5200 and coated with a powder coat, samples of both AM50 and AE44 alloys showed drastic improvement in failure life, i.e., by at least 200%. Although the test conditions used are far more severe than that likely to be encountered during an automotive lifetime, these results have mechanistic implications for all applications where a high loading stress is present.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".