A multi-access edge computing enabled framework for the construction of a knowledge-sharing intelligent machine tool swarm in Industry 4.0. (February 2023) - Zhang, Chao; Zhou, Guanghui; Li, Jingjing; Chang, Fengtian; Ding, Kai; Ma, Dongxu
Abstract: Developing intelligent machine tools has been front and center for manufacturing enterprises to take a step towards intelligent manufacturing in Industry 4.0, which has attracted increasing attention from both academics and industry. Nevertheless, most current approaches focus on the construction of a single digital twin machine tool with limited intelligence due to the lack of data and knowledge accumulated by that machine tool for decision-making support. Consequently, this paper integrates digital twin with multi-access edge computing (MEC) and proposes a novel framework for the construction of a knowledge-sharing intelligent machine tool swarm that supports the secure knowledge sharing across the authorized machine tools in the swarm with ultra-low latency performance. Then, three key enabling methodologies of the framework are introduced from the perspective of digital twin machine tool swarm construction, knowledge-based cloud brain learning, and MEC-enhanced system deployment. Finally, a prototype system is implemented, where its application examples and evaluation experiments demonstrate the feasibility and effectiveness of the proposed approach. Highlights: A novel MEC-enabled framework for knowledge-sharing DTMT swarm is proposed. A systematic modelling and evaluation method ensures the high-fidelity of DTMT. MEC enhances the secure knowledge sharing across DTMTs with ultra-low latency. The prototype provides a reference for industrial implementation of the approach.