211129_IEEE_speed_torque_optimization_GAN_to_TechRxiv.pdf (1.16 MB)
Download file

Automatic Design System with Generative Adversarial Network and Convolutional Neural Network for Optimization Design of Interior Permanent Magnet Synchronous Motor

Download (1.16 MB)
posted on 2021-12-08, 23:03 authored by Yuki ShimizuYuki Shimizu, Shigeo Morimoto, Masayuki Sanada, Yukinori Inoue
The optimal design of interior permanent magnet synchronous motors requires a long time because finite element analysis (FEA) is performed repeatedly. To solve this problem, many researchers have used artificial intelligence to construct a prediction model that can replace FEA. However, because the training data are generated by FEA, it takes a very long time to obtain a sufficient amount of data, making it impossible to train a large-scale prediction model. Here, we propose a method for generating a large amount of data from a small number of FEA results using machine learning. An automatic design system with a deep generative model and a convolutional neural network is then constructed. With its sufficient data, the proposed system can handle three topologies and three motor parameters in a wide range of current vector regions. The proposed system was applied to multi-objective optimization design, with the optimization completed in 13-15 seconds.


JST, ACT-X Grant Number JPMJAX20AE, Japan


Email Address of Submitting Author

ORCID of Submitting Author


Submitting Author's Institution

Osaka Prefecture University

Submitting Author's Country

  • Japan