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基于PSO-BP和NSGA-II的半球共振器的设计和优化

Jinghao Liu¹, Pinghua Li¹, Xuye Zhuang¹

¹College of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China.

|May 27, 2023

概括

这项研究使用先进的算法优化了微电机械系统 (MEMS) 半球共振器陀螺仪 (HRGs). 它在技术限制内确定最佳的共振器结构,在没有物理原型设计的情况下提高性能.

科学领域:

微电机系统 (MEMS) 是指微电机系统.
机械工程机械工程
材料科学材料科学材料科学

背景情况:

MEMS半球共振器陀螺仪 (HRGs) 是高性能,但受到技术和过程限制的限制.
在这些限制下实现最佳的共振器结构对于陀螺仪的进步至关重要.

研究的目的:

为了优化MEMS多半球共振器的设计.
在特定的技术和工艺限制范围内确定最佳的共振器结构.
为HRG设计提供一种经济的替代实验处理.

主要方法:

利用热弹性模型和工艺特征来确定关键的几何参数.
采用有限元模拟来理解性能参数/几何特征关系.
集成的粒子群优化 (PSO) 带有反向传播 (BP) 神经网络用于参数映射.
应用非主导排序基因算法II (NSGA-II) 进行结构参数优化.

主要成果:

确定了具有重大影响共振器性能的特定几何参数.
开发了一个PSO优化的BP神经网络来映射性能以结构参数.
NSGA-II产生了一个优化的共振器结构,其Q因子为42,454,频率差为8539.
通过有限元分析证明,优化结构的性能优于原始设计.

结论:

该研究提出了一种有效和经济的计算方法,用于设计和优化MEMS HRGs.
开发的方法克服了技术和工艺障碍,以实现卓越的共振器设计.
这种优化策略在定义的制造约束范围内提高了多半球共振器的性能.

关键词:

在BP神经网络中,神经网络成员HRGHRG的成员在NSGA-II中,NSGA-II是最重要的.公共服务人员 (PSO)

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