Improving the performance of magnesium alloys for automotive applications
Bibliographic record
Abstract
Magnesium and its alloys are attractive to the automotive industry for their inherent light-weight which leads to highly fuel-efficient design.However, due to a low melting temperature (650°C), magnesium has relatively poor elevated temperature mechanical properties, e.g., creep.This has, therefore, restricted its use in applications such as engine components.Magnesium is also a highly reactive metal and has inherently poor corrosion and wear resistance.Improved corrosion and wear performance can be obtained through alloying and microstructural engineering.However, for enhanced corrosion and tribological properties, the use of surface engineering techniques involving coatings is mandatory.Plasma Electrolytic Oxidation (PEO), also known as "Micro-Arc Oxidation (MAO)", has been used to successfully produce oxide layers on magnesium alloys with excellent tribological and corrosion resistant properties.By controlling the PEO process parameters, uniform, relatively pore-free and well adhered coatings can be produced which can provide adequate corrosion protection.The coating requirements for good tribological properties are somewhat different than for good corrosion performance.However, good tribological performance combined with good corrosion performance can be obtained through control of the PEO processing parameters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".