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首页> 外文期刊>International Journal of Vehicle Noise and Vibration >Effect of multi-parameters interaction on transmission gear rattle based on RBF neural network
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Effect of multi-parameters interaction on transmission gear rattle based on RBF neural network

机译:基于RBF神经网络的多参数交互对传动齿轮拨浪流的影响

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摘要

This paper proposes a method to analyse multi-parameters interaction on transmission gear rattle. Firstly, a simulation model of manual transmission was established, and the angular velocity of each loose gear as well as the mesh force were obtained. Then the loose gear angular velocity was measured on a manual transmission gearbox to verify the model. The derivative of gear mesh force was taken as the rattle index (jerk index), and was calculated using forward difference method. A radial basis function (RBF) neural network was applied to map the relationships between the selected input parameters and rattle index. The results show that gear backlash has the largest influence on gear rattle, followed by the inertia of the loose gear, the speed fluctuation and the drag torque. This study can be easily extended to other types of transmission systems to control the gear noise and improve sound quality.
机译:本文提出了一种分析传动齿轮拨浪鼓多参数交互的方法。 首先,建立了手动变速器的仿真模型,获得了每个松散齿轮的角速度以及网状力。 然后在手动变速箱上测量松动的齿轮角速度,以验证该模型。 齿轮啮合力的衍生物作为拨浪鼓指数(JERK指数),并使用前向差异方法计算。 应用径向基函数(RBF)神经网络来映射所选输入参数和拨浪鼓指数之间的关系。 结果表明,齿轮间隙对齿轮拨浪鼓的影响最大,然后是松动齿轮的惯性,速度波动和拖曳扭矩。 该研究可以很容易地扩展到其他类型的传动系统,以控制齿轮噪声并提高音质。

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