Influence of Pads and Brake Disc wear on Brake Squeal Noise
Bibliographic record
Abstract
The present work aims to investigate the influence of wear of the pads and brake disc on the brake squeal behavior with the help of the Finite Element tool. Brake discs basically work by the pressure of the brake pads against a rotating disc. The friction between the pads and the disc causes the latter to decelerate, but it can also cause dynamic instabilities of the system giving rise to noises. Among the main noise in vehicle brake systems, there is the squeal noise, which is usually associated with the coupling of two neighboring natural modes. One possible way to identify unstable modes is by extracting complex eigenvalues from the system. An unstable mode can be identified when, in the result of the extraction of the complex eigenvalues, the real part of the eigenvalue is positive. In the present work, a brake system (disc and positioned pads and their respective materials and friction coefficients) was duly modeled and validated. The validation was done by means of a correlation between the frequency of the noises found experimentally and the frequency of the unstable modes found in the virtual model. A parametric analysis was performed simulating the wear of the disks and pads to understand the effect of the mass variations and stiffness on the instability of the system. According to the results found in the analyzes that simulate the wear, with the decrease, mainly the thickness of the brake pads, there was an increase of numbers of unstable modes, that is, the brake system is more prone to squeal generation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".