Improvement of Wear Resistance of AZ31 B Mg Alloy by Applying Oxide-Sic Nanocomposite Coating via Plasma Electrolytic Oxidation
Bibliographic record
Abstract
Ceramic coatings were produced on the surface of AZ31 B Mg alloy using a plasma electrolytic oxidation (PEO) process from an aluminate-silicate electrolyte containing SiC nanoparticles at different coating times.Scanning electron microscopy equipped with energy dispersive x-ray spectroscopy was employed to monitor morphological and chemical changes of obtained oxide coatings.It was found that in presence of SiC nanoparticles, porosity as well as mean diameter of pores decreased.Meanwhile, the mean diameter of pores increased with prolonging coating time.The wear tests were conducted using a pin-on-disk tribometer under normal load of 5 N for 1500 cm.The wear results showed that the wear rate of ceramic-SiC nanocomposite coatings was less than ceramic ones.The higher hardness of ceramic-SiC nanocomposite coatings could be the main reason of decrease in wear of these coatings in comparison with simple coatings.
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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) | 0.000 | 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".