Research on the Architecture of Artificial Intelligence-Driven Personalized Customization Industrial Design System
TL;DR
Experiments show that the proposed human–machine collaborative system for mobile and immersive scenes can significantly improve the design efficiency and user experience, reduce the decision-making delay, provide a reference for small and medium-size customization platforms, and provide empirical support for artificial intelligence–enhanced, immersive human–computer interaction.
Abstract
Driven by consumers' personalized needs, 3D-printed jewelry urgently needs a new design paradigm that takes into account creative expression and manufacturing feasibility. In this paper, a human–machine collaborative system for mobile and immersive scenes is proposed: the cloud uses a diffusion model and a generative adversarial network to generate diversified drafts, and the edge side evaluates printability, material cost, and surface quality in real time. Users preview and interact with each other through the virtual reality/augmented reality interface. A five-dimensional preference signal is constructed based on multimodal feedback such as eye movement, gesture, and score, and the driving model generation strategy adaptively converges to the multi-objective balance of manufacturability, low material consumption, and high perceptual satisfaction. Experiments show that this method can significantly improve the design efficiency and user experience, reduce the decision-making delay, provide a reference for small and medium-size customization platforms, and provide empirical support for artificial intelligence–enhanced, immersive human–computer interaction.