Two-level Topology Optimization of an Electromagnetic Actuator Based on Genetic Algorithm and Neighbourhood Method

In this paper a multi-objective topology optimization method is presented to find an innovative design and to improve the conventional iron core structures, which are used in rotary electrical machines and electromagnetic devices. The goals are to find an optimal material distribution with less material usage, which makes the device lighter, and also considering the magnetic force to remain in a reasonable range. At first, a meta-heuristic method, called Enhanced Binary Genetic Algorithm (EBGA) is used to gain an optimal material distribution. In the next step, as a post-processing task, a Neighborhood method is used to improve the technical results, to remove the voids inside the iron shape and to make the structure more suitable for the manufacturing process according to the nowadays available manufacturing technologies. This method is inspired by the image processing technique. Afterwards, a third type of material (copper) is added to the design domain. Air cells, which surround some parts of the copper coil area, will be replaced by copper. With these three steps the amount of force increases significantly, while a lighter optimal shape is obtained. As a case study, the proposed method is tested on a simple electromagnetic nonlinear actuator. The results of this work show that it is possible to extend the method to different active parts of electrical machines such as permanent magnet, stator and rotor.