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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
Topology optimization of IPM motor with aid of deep learning
| Title: | Topology optimization of IPM motor with aid of deep learning |
| Authors: | Sasaki, Hidenori Browse this author | | Igarashi, Hajime Browse this author →KAKEN DB |
| Keywords: | Topology shape optimization | | deep learning | | finite element method | | IPM motor |
| Issue Date: | 21-Mar-2019 |
| Publisher: | IOS Press |
| Journal Title: | International journal of applied electromagnetics and mechanics |
| Volume: | 59 |
| Issue: | 1 |
| Start Page: | 87 |
| End Page: | 96 |
| Publisher DOI: | 10.3233/JAE-171164 |
| 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. |
| Rights: | The final publication is available at IOS Press through http://dx.doi.org/10.3233/JAE-171219 |
| Type: | article (author version) |
| URI: | http://hdl.handle.net/2115/73870 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 佐々木 秀徳
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