Characterization of surface morphology and its correlation with friction performance of brake pads
Bibliographic record
Abstract
The present work presents the morphology evolution of a brake material surface submitted to braking tests through a laboratory-scale tribometer. Optical microscope images of the material’s surface were obtained for every 10 braking operations. These images were post-processed in appropriate computational software. By means of the image segmentation technique, morphological parameters related to the brake material surface were estimated. The wear rate and also the coefficient of friction resulting from the tests were measured. For the NAO material used in this study, the friction behaviour revealed to be strongly associated with the amount of contact plateaus. Besides, the mean area of the contact plateaus was the main factor responsible for increasing the real contact area of the friction material. The higher wear rate observed in the first braking operations can be mainly attributed to the higher surface roughness measured in this condition. As the braking operations progress, the plateaus becomes flatter and wear rate is reduced. Finally, the image segmentation technique proved adequate for investigating morphological aspects in friction material surface.
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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 | 0.001 | 0.001 |
| 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".