Axial Gap Motor Shape Optimization Using Multi-Objective Based Genetic Programming Technique
Ryoto MAEJIMA, Wataru KITAGAWA, Takaharu TAKESHITA, Makoto KATAOKA, Kenji SHIIGI · Journal of the Japan Society of Applied Electromagnetics and Mechanics · 2023
The goal of this study is to investigate the application of optimization algorithms and their employability in manufacturing. Genetic programming, which is performed in this study, can simulate the evolutionary process of organisms in nature on a computer using a population of individuals with genetic information to search for the optimal solution. In this study, an axial-gap motor with a low profile and high output is aimed, and genetic programming is used to accomplish multi-objective optimal design, utilizing the following four objective functions: average torque, torque ripple, cogging torque, and iron loss. The design aim is the shape of a permanent magnet and teeth, and the analysis is performed using the three-dimensional finite element method. As a result, a model was developed, in which each attribute was improved with a maximum gain of 5.01 % average torque. Furthermore, the significance of this method was demonstrated by visualizing the high-dimensional optimization result data via dimensional compression.