A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

被引:123
|
作者
Huang, Ziqi [1 ]
Shen, Yang [2 ]
Li, Jiayi [3 ]
Fey, Marcel [1 ]
Brecher, Christian [1 ]
机构
[1] Rhein Westfal TH Aachen, Lab Machine Tools & Prod Engn WZL, D-52074 Aachen, Germany
[2] UBTECH North Amer Res & Dev Ctr, Pasadena, CA 91101 USA
[3] Univ Calif Los Angeles, Dept Stat, Los Angeles, CA 90095 USA
关键词
artificial intelligence; machine learning; deep learning; digital twin; digital shadow; Industry; 4.0; sustainability; sustainable smart manufacturing; robotics; review; PRODUCT LIFE-CYCLE; FAULT-DIAGNOSIS; MACHINE-TOOLS; BIG DATA; OPTIMIZATION; INFORMATION; QUALITY; DESIGN; SYSTEM; SHADOW;
D O I
10.3390/s21196340
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human-robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.</p>
引用
收藏
页数:35
相关论文
共 50 条
  • [1] Navigating the Digital Odyssey: AI-Driven Business Models in Industry 4.0
    Ji, Feng
    Zhou, Yonghua
    Zhang, Hongjian
    Cheng, Guiqing
    Luo, Qubo
    JOURNAL OF THE KNOWLEDGE ECONOMY, 2024,
  • [2] Digital twins-based smart manufacturing system design in Industry 4.0: A review
    Leng, Jiewu
    Wang, Dewen
    Shen, Weiming
    Li, Xinyu
    Liu, Qiang
    Chen, Xin
    JOURNAL OF MANUFACTURING SYSTEMS, 2021, 60 : 119 - 137
  • [3] CULTURAL HERITAGE DIGITAL PRESERVATION THROUGH AI-DRIVEN ROBOTICS
    Marchello, G.
    Giovanelli, R.
    Fontana, E.
    Cannella, F.
    Traviglia, A.
    29TH CIPA SYMPOSIUM DOCUMENTING, UNDERSTANDING, PRESERVING CULTURAL HERITAGE. HUMANITIES AND DIGITAL TECHNOLOGIES FOR SHAPING THE FUTURE, VOL. 48-M-2, 2023, : 995 - 1000
  • [4] Leveraging Digital Twins for Smart Hydropower: A Pathway to Industry 4.0
    Krug, Thomas
    Veledar, Omar
    Macher, Georg
    SYSTEMS, SOFTWARE AND SERVICES PROCESS IMPROVEMENT, EUROSPI 2024, PT I, 2024, 2179 : 260 - 272
  • [5] AI-Driven Smart Production
    Kaneko H.
    Goto J.
    Kawai Y.
    Mochizuki T.
    Sato S.
    Imai A.
    Yamanouchi Y.
    SMPTE Motion Imaging Journal, 2020, 129 (02): : 27 - 35
  • [6] The impact of digital twins on the evolution of intelligent manufacturing and Industry 4.0
    Mohsen Attaran
    Sharmin Attaran
    Bilge Gokhan Celik
    Advances in Computational Intelligence, 2023, 3 (3):
  • [7] Digital Twins From Smart Manufacturing to Smart Cities: A Survey
    Mylonas, Georgios
    Kalogeras, Athanasios
    Kalogeras, Georgios
    Anagnostopoulos, Christos
    Alexakos, Christos
    Munoz, Luis
    IEEE ACCESS, 2021, 9 : 143222 - 143249
  • [8] AI and Robotics Leading Industry 4.0
    Bader, Rawad
    Ali, Adnan
    Mirza, Nada Masood
    2022 9TH INTERNATIONAL CONFERENCE ON INTERNET OF THINGS: SYSTEMS, MANAGEMENT AND SECURITY, IOTSMS, 2022, : 114 - 117
  • [9] Digital Twins and Cyber-Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison
    Tao, Fei
    Qi, Qinglin
    Wang, Lihui
    Nee, A. Y. C.
    ENGINEERING, 2019, 5 (04) : 653 - 661
  • [10] Digital Twins in Industry 4.0
    Park, Sangchan
    Maliphol, Sira
    Woo, Jiyoung
    Fan, Liu
    ELECTRONICS, 2024, 13 (12)