Modelling The Digital Twin For Data-Driven Product Development A Literature Review

被引:0
|
作者
Himmelstoss, Henry [1 ]
Bauernhansl, Thomas [1 ,2 ]
机构
[1] Fraunhofer Inst Mfg Engn & Automat IPA, Nobelstr 12, D-70569 Stuttgart, Germany
[2] Univ Stuttgart, Inst Ind Mfg & Management, Allmandring 35, D-70569 Stuttgart, Germany
来源
PROCEEDINGS OF THE CONFERENCE ON PRODUCTION SYSTEMS AND LOGISTICS, CPSL 2023-2 | 2023年
关键词
Digital Twin; Asset Administration Shell; Product Development; Digital Manufacturing; Industry; 4.0; Literature Review;
D O I
10.15488/15287
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Due to advanced connectivity and increasing distribution of product-service, more and more data is available from the products used and produced. Scientific publications often describe that this product data can be applied in product development to make it more efficient and that the digital twin can play a central role in data provision and interoperability. However, less attention is paid to how the digital twin should be designed for this purpose and how it should be adequately modelled for these use cases. Therefore, this paper presents a structured literature review to analyse which methods are already described in science to model digital twins in a target-oriented way for use cases of data-driven product development. Not only are the procedures interesting, but also the type of digital twin for which they are intended and whether they describe the procedure at the level of a rough macrostructure or detailed microstructure.
引用
收藏
页码:634 / 643
页数:10
相关论文
共 50 条
  • [21] Augmented Data-Driven Machine Learning for Digital Twin of Stud Shear Connections
    Roh, Gi-Tae
    Vu, Nhung
    Jeon, Chi-Ho
    Shim, Chang-Su
    BUILDINGS, 2024, 14 (02)
  • [22] An advanced resin reaction modeling using data-driven and digital twin techniques
    Ghnatios, Chady
    Gerard, Pierre
    Barasinski, Anais
    INTERNATIONAL JOURNAL OF MATERIAL FORMING, 2023, 16 (01)
  • [23] Digital twin paradigm: A systematic literature review
    Semeraro, Concetta
    Lezoche, Mario
    Panetto, Herve
    Dassisti, Michele
    COMPUTERS IN INDUSTRY, 2021, 130
  • [24] Data-driven digital twin method for leak detection in natural gas pipelines
    Liang, Jing
    Ma, Li
    Liang, Shan
    Zhang, Hao
    Zuo, Zhonglin
    Dai, Juan
    COMPUTERS & ELECTRICAL ENGINEERING, 2023, 110
  • [25] An advanced resin reaction modeling using data-driven and digital twin techniques
    Chady Ghnatios
    Pierre Gérard
    Anais Barasinski
    International Journal of Material Forming, 2023, 16
  • [26] Digital twin-driven product design, manufacturing and service with big data
    Tao, Fei
    Cheng, Jiangfeng
    Qi, Qinglin
    Zhang, Meng
    Zhang, He
    Sui, Fangyuan
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2018, 94 (9-12) : 3563 - 3576
  • [27] Digital twin-driven product design, manufacturing and service with big data
    Fei Tao
    Jiangfeng Cheng
    Qinglin Qi
    Meng Zhang
    He Zhang
    Fangyuan Sui
    The International Journal of Advanced Manufacturing Technology, 2018, 94 : 3563 - 3576
  • [28] Digital twin technology facilitates precision improvement in complex product assembly: A progressive deduction method of data-driven tolerance allocation
    Zhang, He
    Li, Yuan
    Xue, Dong
    Tong, Xin
    Gao, Baihui
    Yu, Jianfeng
    ADVANCED ENGINEERING INFORMATICS, 2024, 62
  • [29] Towards a digital twin: a hybrid data-driven and mechanistic digital shadow to forecast the evolution of lignocellulosic fermentation
    Lopez, Pau Cabaneros
    Udugama, Isuru A.
    Thomsen, Sune T.
    Roslander, Christian
    Junicke, Helena
    Mauricio-Iglesias, Miguel
    Gernaey, Krist, V
    BIOFUELS BIOPRODUCTS & BIOREFINING-BIOFPR, 2020, 14 (05): : 1046 - 1060
  • [30] Digital Twin and Data-Driven Quality Prediction of Complex Die-Casting Manufacturing
    Liu, Dong
    Du, Yu
    Chai, Wenjie
    Lu, ChangQi
    Cong, Ming
    IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2022, 18 (11) : 8119 - 8128