Digital Shadows as an Enabler for the Internet of Production

被引:5
作者
Schuh, Guenther [1 ]
Guetzlaff, Andreas [1 ]
Sauermann, Frederick [1 ]
Maibaum, Judith [1 ]
机构
[1] Rhein Westfal TH Aachen, Werkzeugmaschinenlab WZL, Campus Blvd 30, D-52074 Aachen, Germany
来源
ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: THE PATH TO DIGITAL TRANSFORMATION AND INNOVATION OF PRODUCTION MANAGEMENT SYSTEMS, PT I | 2020年 / 591卷
关键词
Digital shadow; Internet of production; Production planning and control; MANUFACTURING SYSTEM; INDUSTRY; 4.0; TWIN; SIMULATION; SUPPORT; FUTURE;
D O I
10.1007/978-3-030-57993-7_21
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Due to increasing atomization, manufacturing companies generate increasing amounts of production data. Most of this data is domain-specific, heterogeneous and unstructured. This complicates the access, interpretation, analysis and usage for efficiency improvement, faster reaction to change and weaknesses identification. To overcome this challenge, the idea of an "internet of production" is to link all kind of production relevant data by a data lake. Based on this data lake, digital shadows aggregate data for a specific purpose. For example, digital shadows in production planning and control help to manage the dynamic changes like delays in production or machine break-downs. This paper examines the existing research in the field of digital twins and digital shadows in manufacturing and gives a brief overview of the historical development. In particular, the potential and possible applications of digital shadows in production planning and control are analyzed. A top-down-bottom-up approach is developed to support the design of digital shadows in production planning and control.
引用
收藏
页码:179 / 186
页数:8
相关论文
共 33 条
  • [1] The Digital Shadow of production - A concept for the effective and efficient information supply in dynamic industrial environments
    Bauernhansl, Thomas
    Hartleif, Silke
    Felix, Thomas
    [J]. 51ST CIRP CONFERENCE ON MANUFACTURING SYSTEMS, 2018, 72 : 69 - 74
  • [2] Belli Laura., 2019, Frontiers in ICT, V6, P17, DOI [10.3389/fict.2019.00017, DOI 10.3389/FICT.2019.00017]
  • [3] Brecher C, 2019, IEEE INT CONF ROBOT, P9327, DOI [10.1109/icra.2019.8793488, 10.1109/ICRA.2019.8793488]
  • [4] Digital twin as enabler for an innovative digital shopfloor management system in the ESB Logistics Learning Factory at Reutlingen - University
    Brenner, Beate
    Hummel, Vera
    [J]. 7TH CONFERENCE ON LEARNING FACTORIES (CLF 2017), 2017, 9 : 198 - 205
  • [5] Data and knowledge mining with big data towards smart production
    Cheng, Ying
    Chen, Ken
    Sun, Hemeng
    Zhang, Yongping
    Tao, Fei
    [J]. JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION, 2018, 9 : 1 - 13
  • [6] Doyle R., 2012, Nat. Aeronaut. Space Adm., V2012, P1, DOI DOI 10.17226/13354
  • [7] Digital-Twin-Based Job Shop Scheduling Toward Smart Manufacturing
    Fang, Yilin
    Peng, Chao
    Lou, Ping
    Zhou, Zude
    Hu, Jianmin
    Yan, Junwei
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2019, 15 (12) : 6425 - 6435
  • [8] Data-driven production control for complex and dynamic manufacturing systems
    Frazzon, Enzo M.
    Kueck, Mirko
    Freitag, Michael
    [J]. CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2018, 67 (01) : 515 - 518
  • [9] Characterising the Digital Twin: A systematic literature review
    Jones, David
    Snider, Chris
    Nassehi, Aydin
    Yon, Jason
    Hicks, Ben
    [J]. CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY, 2020, 29 : 36 - 52
  • [10] Digital Twin in manufacturing: A categorical literature review and classification
    Kritzinger, Werner
    Karner, Matthias
    Traar, Georg
    Henjes, Jan
    Sihn, Wilfried
    [J]. IFAC PAPERSONLINE, 2018, 51 (11): : 1016 - 1022