Robust Design of a Low-Cost Permanent Magnet Motor with Soft Magnetic Composite Cores Considering the Manufacturing Process and Tolerances

Liu, Chengcheng; Lei, Gang; Ma, Bo; Guo, Youguang; Zhu, Jianguo

doi:10.3390/en11082025

Cited by 11 publications

(5 citation statements)

References 41 publications

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“…Fuzzy optimization has been employed to design electromagnetic devices, including different types of motors [119][120][121]. In addition, fuzzy method has been combined with Taguchi method to address multi-objective optimization of electromagnetic devices [122][123][124][125][126][127][128]. Taguchi method has a drawback of handling robust multi-objective optimization of electromagnetic devices.…”

Section: Fuzzy Optimizationmentioning

confidence: 99%

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Lei

Bramerdorfer

et al. 2021

Applied Sciences

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This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.

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Section: Fuzzy Optimizationmentioning

confidence: 99%

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Lei

Bramerdorfer

et al. 2021

Applied Sciences

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“…In this article, the weighted sum approach 15 is used to implement the MO sensitivity analysis. The combination of the weighted sum approach and MO Taguchi optimization method 35 is applied to identify essential parameters on objectives. The weighted sum of the objectives defined as Equation (21).…”

Section: Sensitivity Analysis Based On Doementioning

confidence: 99%

Sensitivity analysis and multiobjective design optimization of flux switching permanent magnet motor using MLP‐ANN modeling and NSGA‐II algorithm

Mahmouditabar

Vahedi

Mosavi

et al. 2020

Int Trans Electr Energ Syst

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Summary The flux switching permanent magnet (FSPM) motor is relatively a new topology of the permanent magnet (PM) motors, which both PM and armature winding are placed at the stator. This feature leads to more robust design, better heat dissipation, and a proper option for a wide range of industrial applications. The critical part of the development of FSPM motor is the design optimization of the structure to improve the electromagnetic performance of the motor. In this article, first to reduce the computation time and required memory of the optimization procedure, the multiobjective sensitivity analysis based on design of experiment is performed to specify the most effective parameters on the objectives. Then, the initial samples data of the optimization procedure is obtained by 2D finite element method (FEM) model of the FSPM motor, which is validated by the prototype of the motor. Furthermore, based on FEM results the multilayers perceptron artificial neural network for the approximation of relation between design variables and objectives is implemented. Finally, using the nondominated sorting genetic algorithm‐II, the optimization procedure of the FSPM motor is done. The accuracy of the presented optimization procedure is validated by a comparison of the initial prototype and final design of the motor.

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“…Kim [18] confirmed the superior performance of an axial-flux PMSM core material using SMC over silicon steel at a high-speed region. Furthermore, the SMC motors made by powder metallurgy are suitable for 3D integrated structures without assembly and meet competitive price in mass production [19][20][21][22]. In 2019, Wang et al [23] made the smallest SMC BLDC PM motor, with an external diameter of 40.6 mm and a length of 32.5 mm, for small aircraft electric motors.…”

Section: Introductionmentioning

confidence: 99%

Investigation of an Inset Micro Permanent Magnet Synchronous Motor Using Soft Magnetic Composite Material

Pang¹,

Shi²,

Xie³

et al. 2020

Energies

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This paper presents the world’s smallest inset permanent magnet synchronous motor (PMSM) with a soft magnetic composite (SMC) core, providing ease of manufacturing for micromachine applications without silicon steel laminations. The inset motor can offer an additional reluctance torque and higher torque density with a lower usage amount of permanent magnet. A 15 mm diameter inset motor was developed with the thickness of a tile-type permanent magnet which is limited to 1 mm by the manufacturer. The motor was designed with high torque density and low torque ripple by varying the interpole iron width for the rotor. Two inset motors were made using both SMC and silicon steel materials for comparison. The performance of the SMC motor was inferior to the silicon steel motor, but it still meets the specifications of the commercial market. If the thickness of the tile-type permanent magnet is further reduced, the micro inset motor with a SMC core can be easily mass-manufactured using powder sintering.

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Robust Design of a Low-Cost Permanent Magnet Motor with Soft Magnetic Composite Cores Considering the Manufacturing Process and Tolerances

Cited by 11 publications

References 41 publications

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Sensitivity analysis and multiobjective design optimization of flux switching permanent magnet motor using MLP‐ANN modeling and NSGA‐II algorithm

Investigation of an Inset Micro Permanent Magnet Synchronous Motor Using Soft Magnetic Composite Material

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