Brake Noise Reduction Method Based on Monte Carlo Sampling and Particle Swarm Optimization
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, dynamomete...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/8878223 |
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| _version_ | 1850162509495926784 |
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| author | Yihong Gu Yucheng Liu Congda Lu |
| author_facet | Yihong Gu Yucheng Liu Congda Lu |
| author_sort | Yihong Gu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fe5cb92e09cb426a97ca0098626f02e3 |
| institution | OA Journals |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-fe5cb92e09cb426a97ca0098626f02e32025-08-20T02:22:33ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/88782238878223Brake Noise Reduction Method Based on Monte Carlo Sampling and Particle Swarm OptimizationYihong Gu0Yucheng Liu1Congda Lu2College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, ChinaZhejiang Wanda Steering Gear Co., Ltd., Hangzhou 311258, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, ChinaBrake 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.http://dx.doi.org/10.1155/2021/8878223 |
| spellingShingle | Yihong Gu Yucheng Liu Congda Lu Brake Noise Reduction Method Based on Monte Carlo Sampling and Particle Swarm Optimization Shock and Vibration |
| title | Brake Noise Reduction Method Based on Monte Carlo Sampling and Particle Swarm Optimization |
| title_full | Brake Noise Reduction Method Based on Monte Carlo Sampling and Particle Swarm Optimization |
| title_fullStr | Brake Noise Reduction Method Based on Monte Carlo Sampling and Particle Swarm Optimization |
| title_full_unstemmed | Brake Noise Reduction Method Based on Monte Carlo Sampling and Particle Swarm Optimization |
| title_short | Brake Noise Reduction Method Based on Monte Carlo Sampling and Particle Swarm Optimization |
| title_sort | brake noise reduction method based on monte carlo sampling and particle swarm optimization |
| url | http://dx.doi.org/10.1155/2021/8878223 |
| work_keys_str_mv | AT yihonggu brakenoisereductionmethodbasedonmontecarlosamplingandparticleswarmoptimization AT yuchengliu brakenoisereductionmethodbasedonmontecarlosamplingandparticleswarmoptimization AT congdalu brakenoisereductionmethodbasedonmontecarlosamplingandparticleswarmoptimization |