Digital Genealogy: AIGC-Driven Evolution of Digital Twin for Future Smart Manufacturing

To meet higher requirements of flexible manufacturing, smart manufacturing is developing to intelligently deal with changing demands in product customization with better generalization and adaptation. For instance, robotic systems are anticipated to realize embodied and spatial intelligence in manufacturing, to intelligently generalize in handling diverse objects in changing environments. However, the insufficiency of 3D scene data significantly hinders embodied and spatial intelligence learning. Therefore, based on digital twin, the digital genealogy is proposed to generate more diverse synthetic data, rather than synchronizing the same scene with physical world by digital twin. Also, the digital genealogy focuses on the whole evolution process from industrial parts to products in manufacturing, rather than narrowly focusing on current state in digital twin. In digital genealogy, DG-DNA for various industrial parts, similar with biology, is proposed to constrain reliable generation results of parts. To generate digital genealogy scenes with diverse industrial parts, parts matching and generation methods are both adopted with constraints of DG-DNA. Specifically, an artificial intelligence generative algorithm, named DGIP-Gen, is proposed to generate target industrial part given specific DG-DNA. The experimental results have demonstrated the generated parts are diverse and meet the specific constraints of different DG-DNA requirements, to support embodied and spatial intelligence learning.