Prediction of Current-Dependent Motor Torque Characteristics Using Deep Learning for Topology Optimization
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 t...
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| Veröffentlicht in: | IEEE transactions on magnetics 2022-09, Vol.58 (9), p.1-4 |
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| container_title | IEEE transactions on magnetics |
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| creator | Aoyagi, Taiga Otomo, Yoshitsugu Igarashi, Hajime Sasaki, Hidenori Hidaka, Yuki Arita, Hideaki |
| description | 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. |
| doi_str_mv | 10.1109/TMAG.2022.3167254 |
| format | Article |
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| source | IEEE Electronic Library (IEL) |
| subjects | Artificial neural networks Computational modeling Control methods Convolutional neural networks Convolutional neural networks (CNNs) deep learning (DL) Genetic algorithms Machine learning Magnetism Network topology Optimization permanent magnet motor Topology Topology optimization topology optimization (TO) Torque |
| title | Prediction of Current-Dependent Motor Torque Characteristics Using Deep Learning for Topology Optimization |
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