Surface Effect of a PEO Coating on Friction at Different Sliding Velocities
Bibliographic record
Abstract
In order to reduce the weight of an automotive engine, an aluminum (Al) alloy engine block with cast iron liner has been successfully used to replace the gray cast iron engine. For newly emerging Al linerless engine in which the low surface hardness of the aluminum alloy has to be overcome, a few surface processing technologies are used to protect the surface of cylinders. Among them, plasma transferred wire arc (PTWA) thermal spraying coating is becoming popular. Plasma electrolytic oxidation (PEO) coating is also proposed for increasing the wear resistance of aluminum alloy and reducing the friction between the cylinder and piston. In this work, a PEO coating with a thickness of ∼20 μm was prepared, and a high speed pin-on-disc tribometer was used to study the tribological behavior of the coating at oil lubricant conditions. Different surface roughness of the coating and a large range of the sliding speeds were employed for the tests. The coefficient of friction (COF) vs surface roughness and sliding speed were particularly studied. The results show that the COF significantly decreased with the increase of sliding speed, and a smoother coating surface had a lower COF.
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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 | 0.001 |
| 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) | 0.002 | 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".