Construction technology of intelligent manufacturing service systems driven by industrial big data

被引：0
|
作者：
Zhang W. [1 ,2 ,3 ]
Wang X. [1 ]
Shi Y. [2 ]
Gu X. [3 ]
Wang J. [1 ]
Tian J. [1 ]
机构：
[1] Zhejiang Provincial Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology and Equipment, Zhejiang Normal University, Jinhua
[2] Institute for Manufacturing, Department of Engineering, University of Cambridge, Cambridge
[3] The National Key Laboratory of Fluid Power Fundamental and Mechatronic System, Zhejiang University, Hangzhou
来源：
Zhongguo Kexue Jishu Kexue/Scientia Sinica Technologica | 2023年 / 53卷 / 07期
关键词：
industrial big data; intelligent manufacture service; manufacturing service combination optimization; modularization; particle swarm optimization algorithm;
D O I：
10.1360/SST-2022-0372
中图分类号：
学科分类号：
摘要：
To respond quickly to the manufacturing service needs of end users, a construction method for intelligent manufacturing service systems is proposed. Based on intelligent manufacturing service activity big data, this method transforms manufacturing service requirements into intelligent manufacturing service schemes and then calls the matching scheme of an intelligent manufacturing service module, which is integrated into the intelligent manufacturing service system and provided to the end users. Based on a big data analysis of intelligent manufacturing service activities, such as an intelligent factory, intelligent production, and intelligent service, a modular process model of an intelligent manufacturing service system (IMSS) is established. This model describes the mapping relationship of modular processes in terms of the user, functional, manufacturing service, process, and delivery domains. Combined with the industrial big data and an improved genetic bee colony algorithm, an optimal selection scheme for the service module combination in the IMSS is made, and an improved genetic bee colony algorithm based on reverse learning is proposed to optimize the service module composition in intelligent manufacturing. Finally, through the example analysis of automobile personalized service modules, the feasibility of the algorithm and effectiveness of the construction method of IMSSs driven by big data are verified. © 2023 Chinese Academy of Sciences. All rights reserved.
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页码：1084 / 1096
页数：12
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