A practical approach for multiagent manufacturing system based on agent computing nodes. (February 2022)

• Record Type: Journal Article
• Authors: Zhang, Zequn; Tang, Dunbing; Zhu, Haihua; Zhou, Tong; Resendez Pulido, Ana Sheryl; Wang, Liping; Nie, Qingwei
• Other Names: Luo Xichun guest-editor.
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 236:Number 4(2022)
• Page Start: 1907
• Page End: 1930
• Publication Date: 2022-02
• Subjects: Distributed artificial intelligence -- multiagent manufacturing system -- discrete manufacturing workshop -- radio frequency identification -- plug-and-play; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/0954406220908626
• Languages: English
• ISSNs: 0954-4062

Abstract

With the increasing requirement for personalized customization service, discrete manufacturing workshop, as the parts processing unit in manufacturing system, is expected for more agile and fast adaptation to environment changes, dynamically handling production tasks according to resource conditions. Simultaneously, distributed artificial intelligence system (e.g. multiagent manufacturing system and the holonic manufacturing system) has been considered as an important approach for developing industrial applications to solve the problems of complexity, uncertainty, and dynamic in the modern manufacturing environment. But the lack of universality and the difficulty in deployment have restricted the use of distributed artificial intelligence in actual industrial sites. For this issue, a new concept of agent computing node is proposed in this paper to enable the realization of multiagent manufacturing system. Adaptation layer, information development layer, and intelligent analysis layer are investigated for standardizing the configuration mode of agent computing node. Cooperating agent computing node with the radio frequency identification-based dynamic recognition technology for workpiece machining process is presented in this paper, and a practical approach for multiagent manufacturing system is considered, which can apply the functions regarding to deployment of dynamic scheduling and plug-and-play. A laboratory discrete manufacturing workshop system is used as a case study to prove the feasibility of this approach. In addition, a verification in industry is carried out, and the result proves the universality of this approach.