Deep learning-based surrogate model for fast multi-material topology optimization of IPM motor

Abstract

Purpose This paper aims to present a deep learning–based surrogate model for fast multi-material topology optimization of an interior permanent magnet (IPM) motor. The multi-material topology optimization based on genetic algorithm needs large computational burden because of execution of finite element (FE) analysis for many times. To overcome this difficulty, a convolutional neural network (CNN) is adopted to predict the motor performance from the cross-sectional motor image and reduce the number of FE analysis. Design/methodology/approach To predict the average torque of an IPM motor, CNN is used as a surrogate model. From the input cross-sectional motor image, CNN infers dq-inductance and magnet flux to compute the average torque. It is shown that the average torque for any current phase angle can be predicted by this approach, which allows the maximization of the average torque by changing the current phase angle. The individuals in the multi-material topology optimization are evaluated by the trained CNN, and the limited individuals with higher potentials are evaluated by finite element method. Findings It is shown that the proposed method doubles the computing speed of the multi-material topology optimization without loss of search ability. In addition, the optimized motor obtained by the proposed method followed by simplification for manufacturing is shown to have higher average torque than a reference model. Originality/value This paper proposes a novel method based on deep learning for fast multi-material topology optimization considering the current phase angle.

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

“…Even after considering these additional time durations, the proposed method works 30% faster than the conventional method. Moreover, once the CNN is trained, it works swiftly for the optimization problems with different weighting coefficients in the cost function (5) and with different constraints.…”

“…However, the current amplitude and phase are assumed constant in this method even though they vary according to the motor control. It has been shown that the d and q axis inductances, which are assumed to be constants, can be predicted using CNN from the cross-sectional image of an IPM motor [5]. This method allows us to find the current condition that maximizes the torque.…”

Multi-material topology optimization using Wachspress interpolations for designing a 3-phase electrical machine stator

“…As an example, we applied it to the MMTO of a complete three-phase PMSM stator. Although three-phase machines are widely used in industrial applications, only their rotor is generally optimized as in Sato and Igarashi [56], and sometimes with the stator teeth [34,35]. When the magnetic source distribution has been addressed, only one electric phase had been considered, as in Labbé and Dehez [36].…”

Automatic Design System With Generative Adversarial Network and Vision Transformer for Efficiency Optimization of Interior Permanent Magnet Synchronous Motor

“…Many studies have been conducted to reduce the time required for the optimal design of advanced IPMSMs by implementing machine learning (ML) methods [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17]. Although ML-focused research requires a certain amount of training time, the computation takes less than 1/1000th of the time compared with FEA upon model completion [15].…”