@inproceedings{74c844b87f2a4bed845ad53c9b017287,
title = "Deep-neural-network-based Electromagnetic Analysis and Optimal Design of Fractional-slot Brushless DC Motor for High Torque Robot Joints",
abstract = "Fractional-slot brushless DC motors (FS-BLDCMs) have the advantages of high torque density and low cogging torque for robot joints. However, finite element analysis (FEA) of the FS-BLDCMs causes time consumption, which obstructs the progress on finding optimal electromagnetic characteristics of the FS-BLDCMs. This paper presents a novel design method to improve the FS-BLDCM motor characteristics and improve the computation efficiency by the deep neural network (DNN). The FS-BLDCM motor performance is optimized by the genetic algorithm and validated by finite element analysis. The computation time between the finite element analysis (FEA) and the deep neural network (DNN) is compared. The result shows the efficiency of the deep neural network.",
keywords = "Deep Neural Network, Electromagnetic Analysis, Fractional-slot Brushless DC Motor, High Torque Density Motor, Motor Optimization",
author = "Anguo Liu and Fei Meng and Hengzai Hu",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 3rd International Conference on Electrical Engineering and Mechatronics Technology, ICEEMT 2023 ; Conference date: 21-07-2023 Through 23-07-2023",
year = "2023",
doi = "10.1109/ICEEMT59522.2023.10262957",
language = "English",
series = "2023 3rd International Conference on Electrical Engineering and Mechatronics Technology, ICEEMT 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "715--719",
booktitle = "2023 3rd International Conference on Electrical Engineering and Mechatronics Technology, ICEEMT 2023",
address = "United States",
}