Automotive motor rotor design synthesis using cWGAN-gp with distortion penalty

Abstract

This study proposes a motor-rotor-shape generation method based on a conditional Wasserstein generative adversarial networks (GAN) with gradient penalty (cWGAN-gp) to perform the initial design of the automotive motor efficiently. We introduced a distortion degree as a regularization in the training process to generate smooth, intuitively understandable, and easy to manufacture shapes. Rotor shapes generated by cWGAN-gp exhibited characteristics similar to a combination of multiple training data with performances close to that of the input label data. Furthermore, smooth shapes were generated by appropriately setting the influence of the distortion degree. The motor shapes could be generated accurately when a sufficient amount of training data existed. These results suggest that training the model with accumulated motor data allows for the rapid generation of new shapes based on input performance criteria in a short period of time.