Text2shape: Intelligent computational design of car outer contour shapes based on improved conditional Wasserstein generative adversarial network

Abstract

To provide technical support for the initial design of products, we propose an innovative text2shape-based technology for intelligent computational design, which can map engineering semantics to functional/structural/Kansei feature spaces to generate product shapes. New energy vehicles were selected as the application object of this technology, as there are many creative ideas for the outer contour design of new energy vehicles. Firstly, a dataset with 2900 + samples was built based on feature engineering (FE) and Kansei engineering (KE). Each sample contains the car’s outer contour shape’s functional, structural, and Kansei features. Secondly, we proposed an improved conditional Wasserstein generative adversarial network (CWGAN) model suitable to the dataset. The generator’s loss in the model is designed to evaluate the authenticity of the generated results, while the discriminator’s loss assesses the conditional matching of these results. Finally, in case studies, the trained CWGAN was compared with the conditional variational auto-encoder (C-VAE), diffusion, Wasserstein generative adversarial network with gradient penalty (WGAN-GP) and style generative adversarial network (StyleGAN) models, demonstrating superior performance.