Prediction of Current-Dependent Motor Torque Characteristics Using Deep Learning for Topology Optimization

Aoyagi, Taiga; Otomo, Yoshitsugu; Igarashi, Hajime; Sasaki, Hidenori; Hidaka, Yuki; Arita, Hideaki

Prediction of Current-Dependent Motor Torque Characteristics Using Deep Learning for Topology Optimization

Aoyagi, Taiga; Otomo, Yoshitsugu; Igarashi, Hajime et al.

IEEE Transactions on Magnetics, 2022, 58(9), 1-4

Permalink : https://hdl.handle.net/2115/87028

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論文情報

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アクセス権	open access
URI	https://hdl.handle.net/2115/87028
タイトル	Prediction of Current-Dependent Motor Torque Characteristics Using Deep Learning for Topology Optimization
著者	Aoyagi, Taiga ; Otomo, Yoshitsugu ; Igarashi, Hajime ; Sasaki, Hidenori ; Hidaka, Yuki ; Arita, Hideaki
著者名 Aoyagi, Taiga
著者名 Otomo, Yoshitsugu
e-Rad_Researcher 90212737 著者名 Igarashi, Hajime
著者名 Sasaki, Hidenori
著者名 Hidaka, Yuki
著者名 Arita, Hideaki
言語	英語
キーワード	Convolutional neural networks
	CNNs
	deep learning
	DL
	permanent magnet motor
	topology optimization
	TO
発行日	2022-09
収録物名	IEEE Transactions on Magnetics
巻(号)	58(9)
ページ	1-4
抄録	In this study, we propose a fast topology optimization (TO) method based on a deep neural network (DNN) that predicts the current-dependent motor torque characteristics using its cross-sectional image. The trained DNN is shown to provide the current condition that provides the maximum torque under the assumed motor control method. The proposed method helps perform TO with a reduced number of field computations while maintaining a high search capability.
資源タイプ	学術雑誌論文
出版タイプ	Accepted Manuscript
権利情報	© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
PISSN	0018-9464
EISSN	1941-0069
NCID	AA00667933
関連情報 (isVersionOf)	出版社版DOI https://doi.org/10.1109/TMAG.2022.3167254