Title: Industrial design pattern optimisation research based on 3D deep learning algorithm

Authors: Yao Wang

Addresses: College of Creative Design, Chongqing City Vocational College, Chongqing, 402160, China

Abstract: The appearance design of industrial products is mainly done manually, which is time-consuming and inconsistent. To improve efficiency and quality, an intelligent design model based on Generative Adversarial Networks (GAN) was developed. The model converts 3D input data into a scene diagram to represent component relationships and hierarchical structures, making it suitable for complex product layouts. By incorporating the variational autoencoder (VAE) principle, the richness of design information is enhanced. GraphVAE is utilised to learn graph features and generate new structures that meet industrial design needs. Experimental results show that with 1000 test samples, the average information entropy of the proposed model, GoogLeNet, and Fast R-CNN is 35.79, 26.17, and 31.25, respectively, indicating high information richness and similarity to input data. This method optimizes the design process, enhances output quality, and reduces computational costs, though its efficiency is lower, making it suitable for less time-sensitive applications.

Keywords: industrial products; appearance design; GAN; artificial intelligence.

DOI: 10.1504/IJWET.2025.149269

International Journal of Web Engineering and Technology, 2025 Vol.20 No.3, pp.333 - 353

Received: 29 Apr 2024
Accepted: 18 Jan 2025
Published online: 21 Oct 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article