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Multi-Objective Topology Optimization of Rotating Machines Using Deep Learning
Title: | Multi-Objective Topology Optimization of Rotating Machines Using Deep Learning |
Authors: | Doi, Shuhei Browse this author | Sasaki, Hidenori Browse this author | Igarashi, Hajime Browse this author →KAKEN DB |
Keywords: | Deep learning (DL) | genetic algorithm (GA) | inner permanent magnet (IPM) motor | multi-objective optimization | topology optimization |
Issue Date: | Jun-2019 |
Publisher: | IEEE (Institute of Electrical and Electronics Engineers) |
Journal Title: | IEEE transactions on magnetics |
Volume: | 55 |
Issue: | 6 |
Start Page: | 7202605 |
Publisher DOI: | 10.1109/TMAG.2019.2899934 |
Abstract: | This paper presents the fast topology optimization methods for rotating machines based on deep learning. The cross-sectional image of electric motors and their performances obtained during a multi-objective topology optimization based on the finite-element method and genetic algorithm (GA) is used for training of the convolutional neural network (CNN). Two different approaches are proposed: 1) CNN trained by preliminary optimization with a small population for GA is used for the main optimization with a large population and 2) CNN is used for screening of torque performances in the optimization with respect to the motor efficiency. |
Rights: | © 2019 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. |
Type: | article (author version) |
URI: | http://hdl.handle.net/2115/74691 |
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: 五十嵐 一
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