A multi-access edge computing enabled framework for the construction of a knowledge-sharing intelligent machine tool swarm in Industry 4.0. (February 2023)

• Record Type: Journal Article
• Authors: Zhang, Chao; Zhou, Guanghui; Li, Jingjing; Chang, Fengtian; Ding, Kai; Ma, Dongxu
• Is Part Of: Journal of manufacturing systems. Volume 66(2023)
• Page Start: 56
• Page End: 70
• Publication Date: 2023-02
• Subjects: Multi-access edge computing -- Digital twin -- Intelligent machine tool -- Industry 4.0 -- Knowledge sharing; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2022.11.015
• Languages: English
• ISSNs: 0278-6125

Abstract

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.