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高速动车组制动模块可靠性分析与优化

Reliability Analysis and Optimization of Braking Module of High-speed EMU
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摘要 为了计算出动车组制动模块在各个工况运行下的薄弱环节并改善其结构性能,该文运用三维设计软件SolidWorks构建出制动模块的三维模型,再通过有限元分析软件ANSYS Workbench对模型进行模态分析和静强度分析,将应力最大的部位视为薄弱环节,并将该部位作为后续优化设计的关键部位。首先建立制动模块有限元模型,对其进行自由模态分析与约束模态分析,初步确定出薄弱环节。再对其进行静强度分析,确定出最大应力对应的具体位置,再次确定出薄弱环节并将两者进行对比分析。最后,建立初始的BP网络模型,引入四种权重系数并利用GA探寻其最优组合方式,进而提升初始BP网络模型的预测精度,开展动车组制动模块多目标可靠性优化设计,为制动模块结构改进与优化提供了理论依据。 In order to calculate the weak links of the braking module in the power train unit under various operating conditions and improve the structural performance,a three-dimensional model of the braking module is constructed by using the three-dimensional design software SolidWorks,and then modal analysis and static strength analysis are conducted on the modal by using the finite element analysis software ANSYS Workbench.The part with the greatest stress is considered as the weak link,and this part is considered as the key part for subsequent optimization design.Firstly,a finite element model of the brake module is established,and free modal analysis and constrained model analysis are performed to preliminarily identify the weak links.Then,the static strength analysis is carried out to determine the specific location corresponding to the maximum structural stress,identifying the weak link again;and then they are compared and analyzed.Finally,the initial BP model is established,four weight coefficients are introduced and GA is used to explore the optimal combination mode,so as to improve the prediction accuracy of the initial BP model and carry out the muti-objective reliability optimization design of the brake module of the EMU,which provides a theoretical basis for the structural improvement and optimization of the brake module.
作者 唐枫 TANG Feng(School of Mechanical and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou 730070,Gansu,China)
出处 《机械研究与应用》 2023年第5期140-142,146,共4页 Mechanical Research & Application
关键词 制动模块 模态分析 静强度分析 可靠性 BP神经网络 brake module modal analysis static strength analysis reliability BP neural network
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