Friction and counterface wear influenced by surface profiles of plasma electrolytic oxidation coatings on an aluminum A356 alloy
Bibliographic record
Abstract
To reduce the fuel consumption and emission of passenger vehicles, aluminum engines have been increasingly used throughout the last 30 years. Since most conventional aluminum alloys have poor wear resistance, various technical solutions have been developed to generate a wear-resistant cylinder bore surface against the sliding piston ring. In this work, the plasma electrolytic oxidation (PEO) process was employed to produce oxide coatings on an Al alloy A356 for Al engine blocks, to protect against the wear attack. The surface morphology and coating thicknesses were tailored by polishing two PEO coatings. A reciprocating sliding tribometer was used to investigate the tribological and wear behavior of the PEO coatings, counterface materials, and that of a state-of-the-art plasma transferred wire arc coating (as a benchmark) under two lubricated conditions. The results show that the PEO coatings have a low coefficient of friction and minimal wear. The variation in tribological behavior and counterface wear among the tested materials was likely due to different topographic features such as skewness and kurtosis caused by microbump distribution, porosity, and valleys on as-prepared, sanded, and polished coating surfaces.
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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".