Low Friction and High Wear Resistance of Plasma Electrolytic Oxidation (PEO)-Coated AZ31 Mg Alloy Sliding against Hydrogenated DLC (a-C-H) at Elevated Temperatures
Bibliographic record
Abstract
Plasma electrolytic oxidation (PEO) treatment of Mg alloys improves their wear resistance by increasing their surface hardness, but also leads to high coefficient of friction (COF) values. The sliding counterfaces and the conditions under which PEO-coated Mg alloys operate affect their COFs. PEO-coated AZ31 alloy sliding against hydrogenated DLC (a-C-H) coated steel yields a low COF of 0.13 under the ambient conditions. The current study investigates the effect of the test temperature on the tribological behavior of PEO-coated AZ31 Mg samples sliding against the a-C-H coated counterface at temperatures up to 300 °C. According to the COF vs. wear rate diagram constructed in the temperature range of 25–250 °C, lower COF values and wear rates were exhibited by PEO-coated AZ31 sliding against a-C-H compared to uncoated AZ31 sliding against a-C-H, and PEO coated AZ31 sliding against an uncoated ASTM 52100 steel. The PEO-coated AZ31 produced the lowest COF of 0.03 at 200 °C. The application of PEO to the Mg alloy automotive cylinder bores running against DLC-coated piston rings and/or PEO-coated Mg alloy pistons running against DLC-coated bores could provide a new approach for the prevention of seizure and hot scuffing in lightweight engines in the temperature range between 150–250 °C.
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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".