open access

https://hdl.handle.net/2115/73870

Topology optimization of IPM motor with aid of deep learning

英語

IPM motor

2019-03-21

IOS Press

International journal of applied electromagnetics and mechanics

59(1)

87-96

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.

学術雑誌論文

Accepted Manuscript

The final publication is available at IOS Press through http://dx.doi.org/10.3233/JAE-171219

1383-5416

AA11051133