Brake Noise Reduction Method Based on Monte Carlo Sampling and Particle Swarm Optimization | Zendy

Yihong Gu | Zendy; Yucheng Liu | Zendy; Congda Lu | Zendy

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Brake Noise Reduction Method Based on Monte Carlo Sampling and Particle Swarm Optimization

Author(s) -

Yihong Gu,

Yucheng Liu,

Congda Lu

Publication year - 2021

Publication title -

shock and vibration

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.418

H-Index - 45

eISSN - 1875-9203

pISSN - 1070-9622

DOI - 10.1155/2021/8878223

Subject(s) - brake , dynamometer , monte carlo method , noise (video) , automotive engineering , particle swarm optimization , finite element method , engineering , control theory (sociology) , computer science , structural engineering , mathematics , algorithm , artificial intelligence , statistics , control (management) , image (mathematics)

Brake noise is one of the principal components of vehicle noise and is also one of the most critical measures of vehicle quality. During the braking process, the occurrence of brake noise has a significant relationship with the working conditions of the brake system. In the present study, dynamometer test data and the finite element method (FEM) were used to analyze the direct and indirect effects of variations in the working parameters on the brake noise, and a brake noise reduction method was developed. With this method, Monte Carlo sampling was used to consider variations in the parameters of the brake lining during the braking procedure, and the particle swarm optimization method was used to calculate the optimal parameter combination for the brake lining. A dynamometer test was carried out to validate the effect of optimization on brake noise mitigation.

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