• CN: 11-2187/TH
  • ISSN: 0577-6686

Journal of Mechanical Engineering ›› 2026, Vol. 62 ›› Issue (11): 90-101.doi: 10.3901/JME.260402

Previous Articles    

6G Industrial Internet Driven Multilevel Manufacturing Networks and Self-adaptive Collaborative Optimal Control Approach

GUO Zhengang1, ZHANG Yingfeng1, LIANG Weicong2, LIN Haifeng2, LI Xiaohua2   

  1. 1. Key Laboratory of Industrial Engineering and Intelligent Manufacturing, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi'an 710072;
    2. Chengdu Aircraft Industrial (Group) Co., Ltd., Chengdu 610092
  • Received:2025-06-06 Revised:2025-09-15 Published:2026-07-29

Abstract: Oriented towards customized production, discrete aviation product manufacturing enterprises are faced with bottlenecks such as high product complexity, a wide variety of materials, cross-generation mixed production, complex part-supporting relationships, uneven digitalization levels, and high barriers to data interconnection. These bottlenecks result in the frequent occurrence of production interruptions and the difficulties of rapid response to interruptions and inter-enterprise collaboration, which have brought huge challenges to a balanced production and on-time delivery. The national major strategic needs of aviation power and manufacturing power are combined, which is aimed at the new needs and challenges of China's aviation product manufacturing enterprises such as group, main, and supporting manufacturers in terms of multilevel networked manufacturing collaborative capabilities and the ability to quickly recover from interruptions. The problem of a networked manufacturing collaborative system and its smart control and management towards the delivery plan is focused on, and a 6G Industrial Internet-driven multilevel manufacturing network and self-adaptive collaborative optimal control approach is proposed. A general model of multilevel complex networks is developed, and the collaborative mechanism of multilevel manufacturing networks is studied. Subsequently, artificial intelligence algorithms are used to analyze the collaborative performance, and a variable-granularity multilevel self-adaptive optimal control model is constructed to respond to interruptions and recover quickly. Important theoretical and technical support is provided in this paper for the application of the networked manufacturing collaborative system capable of active perception, agile response, collaborative optimization, smart control, and management.

Key words: 6G Internet of Things, industrial Internet, smart manufacturing, complex networks, collaborative optimization

CLC Number: