Topology Optimization with Improved Genetic Algorithm of an Electromagnetic Actuator

S. Ruzbehi, I. Hahn
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引用次数: 2

Abstract

In conventional optimization, geometric optimization with changing the boundaries of the geometric shape is common, but in this study, topological optimization is applied to give the designer more freedom to achieve a machine design, which fulfils the customer’s goals. Topological optimization is common in the field of mechanical engineering, but in the design of electrical machines and electromagnetic actuators there is still an open space for its application.Having an electrical machine with high torque and low weight is one of the desired goals, which is investigated in this study. The implementation of the proposed method is applied to an electromagnetic actuator in order to optimize its topology or structure. To assess the validity of the proposed method, while considering the goals, the design of a simple magnetic actuator using a metaheuristic optimization method is presented as a global search. To overcome the generation of intermediate material characteristics in the cells of the design area, a binary genetic algorithm (GA) is used. Also, to enhance the low convergence rate of the GA, several chromosomes for each subarea of the design space are used independently of each other and operate at the same time, therefore, lead to a significant reduction of the computational time. The actuator’s force is calculated to gain the best material distribution in the discretized design area. After the automatic optimization process some manual changes in the material distribution are made to improve the manufacturability. The given design goals have been successfully achieved using the proposed topological optimization method.
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基于改进遗传算法的电磁作动器拓扑优化
在传统的优化中,改变几何形状边界的几何优化是常见的,但在本研究中,拓扑优化的应用赋予了设计师更大的自由度来实现机器设计,从而满足客户的目标。拓扑优化是机械工程领域的常用方法,但在电机和电磁执行器的设计中仍有广阔的应用空间。具有高扭矩和低重量的电机是期望的目标之一,在本研究中进行了研究。将该方法应用于电磁致动器的拓扑结构优化。为了评估所提方法的有效性,在考虑目标的情况下,采用全局搜索的方式,采用元启发式优化方法设计了一种简单的磁致动器。为了克服在设计区域单元中产生中间材料特性的问题,采用了二值遗传算法(GA)。此外,为了提高遗传算法的低收敛率,设计空间的每个子区域的几个染色体相互独立地使用并同时运行,从而大大减少了计算时间。计算了致动器的力,以获得离散设计区域内的最佳材料分布。在自动优化过程之后,对物料分布进行一些人工更改,以提高可制造性。利用所提出的拓扑优化方法,成功地实现了给定的设计目标。
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