Hu Siyang, Manansala Billy, Fitzer Ulrike, Hohlfeld Dennis, Bechtold Tamara
Department of Engineering, Jade University of Applied Sciences, Friedrich-Paffrath-Str. 101, 26389 Wilhelmshaven, Germany.
Institute for Electronic Appliances and Circuits, University of Rostock, Albert-Einstein-Str. 2, 18059 Rostock, Germany.
Micromachines (Basel). 2025 Mar 29;16(4):401. doi: 10.3390/mi16040401.
In this work, we propose a two-phase approach for a fast topology optimization of multi-resonant MEMSs. The approach minimizes the computation effort required to achieve an optimal design. In the first step, we perform a pre-optimization using bi-directional evolutionary structural optimization (BESO). We found in previous research that BESO can achieve optimal MEMS designs in a significantly lower number of iterations when compared to classical density-based methods. However, we encountered convergence issues with BESO towards the end of the optimization. Therefore, we introduced a second, density-based optimization phase to circumvent this issue. Finally, we introduced model order reduction to reduce the optimization time further. The novel approach is benchmarked with the design task of two common multi-resonant MEMS devices: a linear gyroscope and a micromirror. We show that the two-phase approach can achieve an optimal design within 200 iterations. With the addition of MOR, the computation of the goal function can be further reduced by 50% in our examples.
在这项工作中,我们提出了一种用于多谐振微机电系统快速拓扑优化的两阶段方法。该方法将实现最优设计所需的计算量降至最低。第一步,我们使用双向进化结构优化(BESO)进行预优化。我们在先前的研究中发现,与传统的基于密度的方法相比,BESO能够以显著更少的迭代次数实现微机电系统的最优设计。然而,在优化接近尾声时,我们遇到了BESO的收敛问题。因此,我们引入了第二个基于密度的优化阶段来规避这个问题。最后,我们引入了模型降阶以进一步减少优化时间。这种新颖的方法以两种常见的多谐振微机电系统器件的设计任务为基准:线性陀螺仪和微镜。我们表明,两阶段方法能够在200次迭代内实现最优设计。在我们的示例中,通过添加模型降阶,目标函数的计算量可进一步减少50%。