Investigation of Al<sub>2</sub>O<sub>3</sub>-Ni Coated Cast Iron Brake Rotors Under Modified Brake Dynamometer Test Standards
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Due to the reduced or less-frequent usages of the friction brakes and the lower brake rotor temperature on electrical vehicles (EV), corrosion would much likely occur on brake rotors. Using hard braking to clean the corroded rotor surfaces often leads to extra rotor surface wear. Improvement in corrosion and wear resistance is an important technological topic to brake rotors for EVs. Many original equipment manufacturers (OEM) and their suppliers are exploring surface treatments including laser cladding and thermal spray processes on cast iron rotors to combat the corrosion issues. However, mentioned surface coating processes increase the cost of brake rotors and there is a need to search for cost-effective coating processes. In this research, a new Al<sub>2</sub>O<sub>3</sub>-Ni composite coating was proposed for preparation of a commercial cast iron brake rotor using plasma electrolytic aluminating (PEA) followed by electroless nickel plating (ENP) processes. The added nickel was to fit in the intrinsic pores of PEA coating and reached to the coating top surface. The brake rotor with the corrosion-resistant PEA-ENP coating was tested with modified SAE Brake Dynamometer Standards J2522. In the modified brake dynamometer test, the number of stops in all the Fade sections were decreased from 15 to 6 to maintain the test temperature below 500 °C. The paper presents the dynamometer test results including coefficient of friction (COF), surface morphology and wear performance. The average tested COF was around 0.31-0.34 while the coating on the cast iron brake rotor remained integrated after the modified dynamometer test. The test results indicate that the new PEA-ENP coating is a potential process to improve brake rotor wear and corrosion resistance for EVs. Dynamometer tests with unmodified standards will be conducted in the future to further confirm the validity of the PEA-ENP coating applications in actual brake systems.</div></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".