Use Case Driven Digital Twin Generation

被引:0
|
作者
Goellner, Denis [1 ]
Klausmann, Tobias [1 ]
Rasor, Rik [2 ]
Dumitrescu, Roman [2 ]
机构
[1] Lenze SE, Hans Lenze Str 1, Aerzen, Germany
[2] Fraunhofer Inst Mech Syst Design IEM, Zukunftsmeile 1, Paderborn, Germany
来源
2022 IEEE 5TH INTERNATIONAL CONFERENCE ON INDUSTRIAL CYBER-PHYSICAL SYSTEMS, ICPS | 2022年
关键词
Industry; 4.0; Digital Twin; Asset Administration Shell; Semantic Modeling; Information Modeling; Cyber Physical Systems;
D O I
10.1109/ICPS51978.2022.9816907
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Digital twins, especially when standardized, are an essential aspect of Industry 4.0, because they enable interoperability for components of different companies, both in the engineering and operational phase. The German initiative "Plattform Industrie 4.0" considers the Asset Administration Shell (AAS) as the Digital Twin for Industry 4.0. The concept provides a meta model and initial submodels, each of which contains the information needed for a common use case. For a variety of use cases, in particular customer specific applications, the information that may need to be provided by multiple AASs to ensure the correct execution of the application must be specified by the partners involved. This contribution presents an approach and an architecture for a model-based generation of AASs. The foundation is a model containing the specification of a use case. Each partner involved in the execution of this use case uses an instance of the presented Digital Twin Generator to create the required AAS. The Digital Twin Generator analyzes the required information based on the provided model, finds it in a company's tools, in databases or on the physical twin and publishes the generated AAS on a web server.
引用
收藏
页数:6
相关论文
共 50 条
  • [21] Use of digital methods to realize a digital twin of bridges
    Koehncke, Martin
    Hamdan, Al-Hakam
    Bartnitzek, Jens
    Henke, Sascha
    Kessler, Sylvia
    BAUTECHNIK, 2025, 102 (03) : 167 - 176
  • [22] Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing
    Zhou, Guanghui
    Zhang, Chao
    Li, Zhi
    Ding, Kai
    Wang, Chuang
    INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2020, 58 (04) : 1034 - 1051
  • [23] Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis
    Warke, Vivek
    Kumar, Satish
    Bongale, Arunkumar
    Kotecha, Ketan
    SUSTAINABILITY, 2021, 13 (18)
  • [24] Bill of material consistency reconstruction method for complex products driven by digital twin
    Wang, Yunrui
    Ren, Wenzhe
    Zhang, Chuanwei
    Zhao, Xuwen
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2022, 120 (1-2) : 185 - 202
  • [25] A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin
    Luo, Weichao
    Hu, Tianliang
    Ye, Yingxin
    Zhang, Chengrui
    Wei, Yongli
    ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2020, 65
  • [26] The Digital Factory Twin - An Empirical Study of Use Cases and Challenges
    Burggräf P.
    Adlon T.
    Schäfer N.
    ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb, 2023, 118 (03): : 178 - 182
  • [27] Review of digital twin applications in manufacturing
    Cimino, Chiara
    Negri, Elisa
    Fumagalli, Luca
    COMPUTERS IN INDUSTRY, 2019, 113
  • [28] A Digital Twin-Driven Methodology for Material Resource Planning Under Uncertainties
    Luo, Dan
    Thevenin, Simon
    Dolgui, Alexandre
    ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE AND RESILIENT PRODUCTION SYSTEMS, APMS 2021, PT I, 2021, 630 : 321 - 329
  • [29] Digital Twin-Driven Approach for Smart City Logistics: The Case of Freight Parking Management
    Liu, Yu
    Folz, Pauline
    Pan, Shenle
    Ramparany, Fano
    Bolle, Sebastien
    Ballot, Eric
    Coupaye, Thierry
    ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE AND RESILIENT PRODUCTION SYSTEMS, APMS 2021, PT IV, 2021, 633 : 237 - 246
  • [30] Digital twin-driven smart supply chain
    Lu WANG
    Tianhu DENG
    Zuo-Jun Max SHEN
    Hao HU
    Yongzhi QI
    Frontiers of Engineering Management, 2022, 9 (01) : 56 - 70