Wear Performances of Gray Cast Iron Brake Rotor with Plasma Electrolytic Aluminating Coating against Different Pads
Bibliographic record
Abstract
Gray cast iron brake rotor experiences substantial wear during braking and contributes largely to the wear debris emissions. Surface coating on the gray cast iron rotor represents a trending approach dealing with the problems. In this research, a new plasma electrolytic aluminating (PEA) process was used for preparing an alumina-based ceramic coating with metallurgical bonding to the gray cast iron. Three different types of brake pads (ceramic, semi-metallic and non asbestos organic (NAO)) were used for tribotests. Performances of PEA coatings vs. different brake pad materials were comparatively investigated with respect to their coefficients of friction (COFs) and wear. The PEA-coated brake rotor has a dimple-like surface which promotes the formation of a thin transferred film to protect the rotor from wear. The transferred film materials come from the wear debris of the pads. The secondary plateaus are regenerated on the brake pads through compacting wear debris of the pads. The wear debris is likely transported between the secondary plateaus on the pads and the transferred film on the rotor before the debris is worn off, which would reduce the debris emission. All three kinds of pads showed a similar wear rate and friction level at the room temperature testing condition. The dynamic formation and detachment of the secondary plateaus are responsible to the variation of COFs. Due to its anti-wear and anti-corrosion property, the PEA ceramic coating could be a promising solution to deal with wear, corrosion and emission issues of a brake rotor for conventional and electrical vehicles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".