Recent Trends in Additive Manufacturing and Topology Optimization of Reluctance Machines

被引:6
作者
Hussain, Shahid [1 ]
Kallaste, Ants [1 ]
Vaimann, Toomas [1 ]
机构
[1] Tallinn Univ Technol, Dept Elect Power Engn & Mechatron, EE-19086 Tallinn, Estonia
关键词
additive manufacturing; topology optimization; level set; synchronous reluctance machine; switch reluctance machine; ON-OFF method; material density; genetic algorithm; power bed fusion; binder jetting; soft magnetic materials; LEVEL SET; ELECTRICAL MACHINES; COGGING TORQUE; IPM MOTOR; DESIGN; ROTOR; OPPORTUNITIES; SHAPE;
D O I
10.3390/en16093840
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Additive manufacturing (AM) or 3D printing has opened up new opportunities for researchers in the field of electrical machines, as it allows for more flexibility in design and faster prototyping, which can lead to more efficient and cost-effective production. An overview of the primary AM techniques utilized for designing electrical machines is presented in this paper. AM enables the creation of complex and intricate designs that are difficult or impossible to achieve using traditional methods. Topology Optimization (TO) can be used to optimize the design of parts for various purposes such as weight, thermal, material usage and structural performance. This paper primarily concentrates on the most recent studies of the AM and TO of the reluctance machines. The integration of AM with TO can enhance the design and fabrication process of magnetic components in electrical machines by overcoming current manufacturing limitations and enabling the exploration of new design possibilities. The technology of AM and TO both have limitations and challenges which are discussed in this paper. Overall, the paper offers a valuable resource for researchers and practitioners working in the field of AM and TO of electrical machines.
引用
收藏
页数:19
相关论文
共 103 条
  • [1] Abetti P.A., 1958, T AM I ELECT ENG PAR, V77, P367, DOI [10.1109/TCE.1958.6372814, DOI 10.1109/TCE.1958.6372814]
  • [2] Transfer Learning Through Deep Learning: Application to Topology Optimization of Electric Motor
    Asanuma, Jo
    Doi, Shuhei
    Igarashi, Hajime
    [J]. IEEE TRANSACTIONS ON MAGNETICS, 2020, 56 (03)
  • [3] Ayat S, 2020, 2020 INTERNATIONAL CONFERENCE ON ELECTRICAL MACHINES (ICEM), VOL 1, P1554, DOI [10.1109/icem49940.2020.9270945, 10.1109/ICEM49940.2020.9270945]
  • [4] Bali M., 2017, P MOR DRIV 2017 MEG
  • [5] Barta J., 2015, MM Science Journal, P555, DOI [DOI 10.17973/MMSJ.2015_03_201504, 10.17973/mmsj.2015_03_201504]
  • [6] Bendsoe MP, 1989, STRUCTURAL OPTIMIZAT, V1, P193, DOI [DOI 10.1007/BF01650949, 10.1007/BF01650949]
  • [7] Modern Electrical Machine Design Optimization: Techniques, Trends, and Best Practices
    Bramerdorfer, Gerd
    Tapia, Juan A.
    Pyrhonen, Juha J.
    Cavagnino, Andrea
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2018, 65 (10) : 7672 - 7684
  • [8] Campelo F., 2010, ACADEMIA, V46, P2010
  • [9] Cavazzuti M., 2012, Optimization methods: from theory to design scientific and technological aspects in mechanics
  • [10] Cederlund J., 2021, P IECON 2021 47 ANN, P1