Topology optimization of IPM motor with aid of deep learning

Article type: Research Article

Authors: Sasaki, Hidenori^a; | Igarashi, Hajime^a

Affiliations: [a] Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan

Correspondence: [*] Corresponding author: Hidenori Sasaki, Laboratory of Applied Electromagnetics, Graduate School of Information Science and  Technology, Hokkaido University, Japan. E-mail: sasaki@em.ist.hokudai.ac.jp

Abstract: This paper presents a new topology optimization of interior permanent magnet (IPM) motors using the genetic algorithm with aid of the deep leaning. The data composed of the rotor shape of an IPM motor and its performance, obtained by a prior topology optimization process, is input to a convolutional neural network (CNN). After the learning process, CNN is shown to provide fairly accurate estimate of the motor performance. During the posterior topology optimization, the finite element analysis (FEA) is carried out only for the limited number of individuals; probability that FEA is performed increases with the motor performance evaluated by CNN. It is shown that the computing time is reduced to about 1/10 without deterioration of the optimization performance with aid of the deep learning.

Keywords: Topology shape optimization, deep learning, finite element method, IPM motor

DOI: 10.3233/JAE-171164

Journal: International Journal of Applied Electromagnetics and Mechanics, vol. 59, no. 1, pp. 87-96, 2019