Improving the performance of magnesium alloys for automotive applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 (650C), 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 it