首页> 外文期刊>NeuroQuantology: an interdisciplinary journal of neuroscience and quantum physics >Analysis of the Radial Stiffness of Rubber Bush Used in Dynamic Vibration Absorber Based on Artificial Neural Network
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Analysis of the Radial Stiffness of Rubber Bush Used in Dynamic Vibration Absorber Based on Artificial Neural Network

机译:基于人工神经网络的动态减振器橡胶衬套径向刚度分析。

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摘要

Rubber bush is used in dynamic vibration absorber as dissipating devices in damping boring bar. These devices actually have to support radial load in compression when chattering occurs. Mastering the behavior of the radial stiffness of the rubber bush implies an accurate understanding of dynamic vibration absorber. The behavior is, however, complex due to the changeable cross-sectional shape and boundary conditions of the rubber bush. By using artificial neural network, the radial stiffness can be predicted efficiently. According to the authors’ knowledge, simulations and tests on radial stiffness of the rubber bush under combined different cross-sectional shape and boundary conditions by using artificial neural network have not been performed yet. The purpose of this study is thus to find the law of radial stiffness of rubber bush under different cross-section shapes and axial pre-compression conditions. In order to achieve this aim, simulations and tests under different chamfering sizes and axial pre-compression by using artificial neural network were first carried out.
机译:橡胶衬套用于动态减振器中,作为阻尼镗杆中的消散装置。这些设备实际上在颤振发生时必须承受径向载荷。掌握橡胶衬套径向刚度的行为意味着对动态吸振器的准确理解。但是,由于橡胶衬套的横截面形状和边界条件变化,因此行为很复杂。通过使用人工神经网络,可以有效地预测径向刚度。根据作者的知识,尚未通过人工神经网络对橡胶套在不同横截面形状和边界条件下的径向刚度进行仿真和测试。因此,本研究的目的是找出橡胶衬套在不同横截面形状和轴向预压缩条件下的径向刚度定律。为了达到这个目的,首先通过人工神经网络进行了不同倒角尺寸和轴向预压的模拟和试验。

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