Effect of Surface Roughness and Sliding Velocity on Tribological Properties of an Oxide-Coated Aluminum Alloy
Bibliographic record
Abstract
Aluminum engines have been successfully used to replace heavy gray cast engines to lighten the car's weight and reduce the fuel consumption. To overcome the aluminum alloys' poor wear resistance, cast iron liners and thermal spraying coatings were used as cylinder bore materials for wear protection. A plasma electrolytic oxidation (PEO) technique had also been proposed to produce an oxide coating on aluminum cylinder bore. The oxide coating can have a low coefficient of friction (COF) and minimum wear shown in the lab tests. To conserve more fuel, the stopping and restarting system was introduced when the vehicle was forced to stop immediately for a short time. When the engine was forced to stop and restart, the reciprocating speed of the piston was very slow, and the friction between the piston and the cylinder was high. In this research, a pin-on-disc tribometer was used to investigate tribological behavior of the oxide coating on an aluminum alloy. The rotational velocity of the tribometer was increased stepwise in a low speed range during the tests. The COF and wear of counterface pins were measured and evaluated corresponding to different combinations of sliding velocities. The results showed that the COF could be affected by many factors such as coating composition, surface roughness, amount of lubricating oil and sliding velocity. With the increase of velocity in the low speed range, the COF decreased. The smoother of the coating surface, the less wear of counterface pin. A proper combination of coating surface roughness and sliding velocity could provide a significant lower COF and less wear.
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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) | 0.001 | 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".