Smart control of the assembly process with a fuzzy control system in the context of Industry 4.0

被引:21
|
作者
Huo, Jiage [1 ]
Chan, Felix T. S. [1 ]
Lee, Carman K. M. [1 ]
Strandhagen, Jan Ola [2 ]
Niu, Ben [3 ]
机构
[1] Hong Kong Polytech Univ, Dept Ind & Syst Engn, Kowloon, Hong Kong, Peoples R China
[2] Norwegian Univ Sci & Technol, Dept Mech & Ind Engn, NO-7491 Trondheim, Norway
[3] Shenzhen Univ, Coll Management, Shenzhen, Peoples R China
关键词
Assembly line balancing; Re-balancing; Industry; 4.0; Fuzzy control system; BALANCING PROBLEM; LINES; ALGORITHM; INTERNET; THINGS; MODEL;
D O I
10.1016/j.aei.2019.101031
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Assembly line balancing is important for the efficiency of the assembly process, however, a wide range of disruptions can break the current workload balance. Some researchers explored the task assignment plan for the assembly line balancing problem with the assumption that the assembly process is smooth with no disruption. Other researchers considered the impacts of disruptions, but they only explored the task re-assignment solutions for the assembly line re-balancing problem with the assumption that the re-balancing decision has been made already. There is limited literature exploring on-line adjustment solutions (layout adjustment and production rate adjustment) for an assembly line in a dynamic environment. This is because real-time monitoring of an assembly process was impossible in the past, and it is difficult to incorporate uncertainty factors into the balancing process because of the randomness and non-linearity of these factors. However, Industry 4.0 breaks the information barriers between different parts of an assembly line, since smart, connected products, which are enabled by advanced information and communication technology, can intelligently interact and communicate with each other and collect, process and produce information. Smart control of an assembly line becomes possible with the large amounts of real-time production data in the era of Industry 4.0, but there is little literature considering this new context. In this study, a fuzzy control system is developed to analyze the real-time information of an assembly line, with two types of fuzzy controllers in the fuzzy system. Type 1 fuzzy controller is used to determine whether the assembly line should be re-balanced to satisfy the demand, and type 2 fuzzy controller is used to adjust the production rate of each workstation in time to eliminate blockage and starvation, and increase the utilization of machines. Compared with three assembly lines without the proposed fuzzy control system, the assembly line with the fuzzy control system performs better, in terms of blockage ratio, starvation ratio and buffer level. Additionally, with the improvement of information transparency, the performance of an assembly line will be better. The research findings shed light on the smart control of the assembly process, and provide insights into the impacts of Industry 4.0 on assembly line balancing.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] A fuzzy control system for assembly line balancing with a three-state degradation process in the era of Industry 4.0
    Huo, Jiage
    Zhang, Jianghua
    Chan, Felix T. S.
    [J]. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2020, 58 (23) : 7112 - 7129
  • [2] Emergent control in the context of industry 4.0
    Garcia, Marcel
    Aguilar, Jose
    [J]. INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2022, 35 (03) : 247 - 262
  • [3] Quality Control in the Context of Industry 4.0
    Godina, Radu
    Matias, Joao C. O.
    [J]. INDUSTRIAL ENGINEERING AND OPERATIONS MANAGEMENT II, IJCIEOM, 2019, 281 : 177 - 187
  • [4] Smart production planning and control in the Industry 4.0 context: A systematic literature review
    Bueno, Adauto
    Godinho Filho, Moacir
    Frank, Alejandro G.
    [J]. COMPUTERS & INDUSTRIAL ENGINEERING, 2020, 149 (149)
  • [5] Industry 4.0 and Smart Systems in Manufacturing: Guidelines for the Implementation of a Smart Statistical Process Control
    Goecks, Lucas Schmidt
    Habekost, Anderson Felipe
    Coruzzolo, Antonio Maria
    Sellitto, Miguel Afonso
    [J]. APPLIED SYSTEM INNOVATION, 2024, 7 (02)
  • [6] A fuzzy control system on industry evaporation process
    Zhang, Z
    [J]. PROCEEDINGS OF THE 3RD WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-5, 2000, : 1646 - 1649
  • [7] PLC 4.0: A Control System for Industry 4.0
    Azarmipour, Mahyar
    Elfaham, Haitham
    Gries, Caspar
    Epple, Ulrich
    [J]. 45TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY (IECON 2019), 2019, : 5513 - 5518
  • [8] Production planing and control in the context of industry 4.0
    Bach T.
    Schuh G.
    Reschke J.
    [J]. ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb, 2019, 114 (12): : 815 - 818
  • [9] Analysis of control architectures in the context of Industry 4.0
    Meissner, Hermann
    Ilsen, Rebecca
    Aurich, Jan C.
    [J]. 10TH CIRP CONFERENCE ON INTELLIGENT COMPUTATION IN MANUFACTURING ENGINEERING - CIRP ICME '16, 2017, 62 : 165 - 169
  • [10] Smart AGV System for Manufacturing Shopfloor in the Context of Industry 4.0
    Theunissen, Jacobus
    Xu, Hang
    Zhong, Ray Y.
    Xu, Xun
    [J]. PROCEEDINGS OF THE 2018 25TH INTERNATIONAL CONFERENCE ON MECHATRONICS AND MACHINE VISION IN PRACTICE (M2VIP), 2018, : 132 - 137