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: Yihong Gu, Yucheng Liu, Congda Lu
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Shock and Vibration
Online Access:http://dx.doi.org/10.1155/2021/8878223
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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
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publishDate 2021-01-01
publisher Wiley
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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