Integrating large language model and digital twins in the context of industry 5.0: Framework, challenges and opportunities

Abstract

In Industry 5.0, where human ingenuity is combined with cutting-edge technologies such as artificial intelligence (AI) and robotics to revolutionize manufacturing with a focus on sustainability and human well-being, Digital Twins (DT) have become essential to real-time optimization. However, the complexity of managing DT for large-scale systems poses challenges in terms of data transmission, analytics, and advanced applications, which can be potentially addressed by Large Language Model (LLM). This research firstly performs a literature review to study the roles and functions of LLM in DT in the context of Industry 5.0. Subsequently, we propose a framework named Interactive-DT for LLM-DT integration that reveals the technical pathway for how LLM can be effectively integrated and function within DT environments. Within this framework, the roles and functionalities of LLM at the edge layer, DT layer, and service layer are elaborated upon. Finally, the identified research gaps and prospects for the integration of LLM and DT are outlined and discussed. The research outcomes of this paper highlight the potential of LLM to augment DT capabilities through improved construction and operation, enhanced cloud-edge collaboration, and sophisticated data analytics, ultimately promoting industrial practices that are both efficient and aligned with human-centric and sustainability principles in Industry 5.0.