A multi-access edge computing enabled framework for the construction of a knowledge-sharing intelligent machine tool swarm in Industry 4.0

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.

Automated summary

Developing intelligent machine tools is crucial for manufacturing enterprises to achieve intelligent manufacturing in Industry 4.0. However, most current approaches focus on single digital twin machine tools with limited intelligence. This paper proposes a novel framework that integrates digital twin with multi-access edge computing (MEC) to construct a knowledge-sharing intelligent machine tool swarm with secure and ultra-low latency performance.