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

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
Hauptverfasser: Aoyagi, Taiga, Otomo, Yoshitsugu, Igarashi, Hajime, Sasaki, Hidenori, Hidaka, Yuki, Arita, Hideaki
Format: Artikel
Sprache:eng
Schlagworte:
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
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Beschreibung
Zusammenfassung: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.
ISSN:0018-9464
1941-0069
DOI:10.1109/TMAG.2022.3167254