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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. [ABSTRACT FROM AUTHOR]

In this article, we propose an interactive visualization method for motor design. We have proposed a visualization system for education using augmented reality (AR) and virtual reality (VR). As a further development of this, we are working on motor design as an industrial application. There are two issues in achieving high-speed interactivity. One is a fast mesh modification method in finite element analysis, and the other is a fast solving method. We propose a high-speed mesh modification method that utilizes regularly arranged grid points and coordinate transformation. In the proposed design system, the user can immediately observe the analysis result of the design change. Furthermore, if the proposed system is executed in cooperation with the projector, designers can change the design of the motor with their fingers and discuss it on the board. Furthermore, since this system can be executed on a Web browser, it can also be executed on a smartphone or tablet PC. [ABSTRACT FROM AUTHOR]

This study presents a novel two-step optimization method that incorporates explainable neural networks into topology optimization. The deep neural network (DNN) is trained to infer the torque performance from the input image of the motor cross section. The sensitive region that has a significant influence on the average torque is extracted using gradient-weighted class activation mapping (Grad-CAM) constructed from the DNN. Then, the optimization with respect to the torque ripple is performed only in the incentive region with little influence on the average torque. The proposed method is shown to increase the average torque of an interior permanent magnet (IPM) motor by 14% and reduce the torque ripple by 79% compared with the original model. [ABSTRACT FROM AUTHOR]

This article proposes a two-step topology optimization method based on the normalized Gaussian functions. The proposed method is shown to be effective for design of a claw-pole alternator. In this method, the global search using the micro-genetic algorithm ($\boldsymbol {\mu }$ GA) is followed by the local search using the sensitivity analysis based on the adjoint variable method. The 3-D structure of the rotor is optimized using the proposed method under low- and high-speed conditions. [ABSTRACT FROM AUTHOR]

The magnetic characteristic of magnetic steel sheets used for rotating machines can be degraded by punching, cutting, or other manufacturing process. This degradation damages the performances of motors. This paper presents topology optimization of rotating machines considering the localized magnetic degradation caused by punching process. It is shown that the optimized synchronous reluctance motors obtained by this method satisfy the torque constraint under the existence of the magnetic degradation. [ABSTRACT FROM AUTHOR]

This paper presents robust topology optimization of electromagnetic machines by considering the magnetic degradation caused by mechanical and thermal stresses in punching, shrinking fitting, and other manufacturing processes. The topology optimization is performed using two methods: one is the robust genetic algorithm in which random noises are added to the magnetic characteristic parameters and the other takes the deviations in the objective and constraint functions due to the degradation into account. These methods are applied to optimization of the flux barrier shapes in an interior permanent magnetic motor to find that one can successfully realize robust design. [ABSTRACT FROM PUBLISHER]