A Simulation Algorithm of a Digital Twin for Manual Assembly Process

被引:14
|
作者
Latif, Hasan [1 ,2 ]
Starly, Binil [1 ]
机构
[1] NC State Univ, Dept Ind & Syst Engn, Raleigh, NC 27606 USA
[2] Raytheon Corp, Charlotte, NC 28217 USA
关键词
Digital Twin; Industry; 4.0; Smart Manufacturing; Adaptive Simulation; HYBRID;
D O I
10.1016/j.promfg.2020.05.132
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Digital twin (DT) is one of the key concepts for Industry 4.0 as it is a critical component in driving real-time simulation and decision making in complex systems. The existing scientific literature on Digital twin primarily refers to a product entity or a physical machine but the core concept can be applied to the entire product lifecycle, particularly the assembly process of a complex product system. In addition, majority of existing work focus on the Digital Twins of individual machines on a shop-floor. This paper focuses on aspects the process to build DTs of a production schedule for a complex defence weapon system. The process is inherently high variety and low quantity in a very manual assembly process. This paper consists of three elements. (1) It reviews the current state of art along with the research gap and discusses how DT can become a tool to the manual assembly process. (2) A data-driven simulation algorithm is proposed to model the complex and manual manufacturing process in a generic-reusable way. (3) Finally, an appropriate complex industrial case study is studied to exemplify the proposed framework. Results demonstrate that the production managers can make more informed early decisions that can help bring assembly schedules in check and limit wasteful efforts when disruptions in the supply chain of parts sourced for the assembly occur. (c) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Scientific Committee of the NAMRI/SME.
引用
收藏
页码:932 / 939
页数:8
相关论文
共 50 条
  • [31] Simulation and Digital Twin of a Robotic Sanitizing Process of Environments at Risk During the Pandemic
    Cepolina, Francesco
    Cepolina, Elvezia Maria
    ROBOTICS IN NATURAL SETTINGS, CLAWAR 2022, 2023, 530 : 501 - 512
  • [32] A Digital Twin Model of Three-Dimensional Shading for Simulation of the Ironmaking Process
    Lei, Yongxiang
    Karimi, Hamid Reza
    MACHINES, 2022, 10 (12)
  • [33] A Digital Twin Platform Integrating Process Parameter Simulation Solution for Intelligent Manufacturing
    Wang, Haoran
    Yang, Zuoqing
    Zhang, Quan
    Sun, Qilei
    Lim, Enggee
    ELECTRONICS, 2024, 13 (04)
  • [34] Process Simulation and Optimization of Arc Welding Robot Workstation Based on Digital Twin
    Zhang, Qinglei
    Xiao, Run
    Liu, Zhen
    Duan, Jianguo
    Qin, Jiyun
    MACHINES, 2023, 11 (01)
  • [35] Digital Twin and Simulation Analyses for Process Optimization of an Automated Guided Vehicle System
    Schulze, Lothar
    Li, Li
    TEHNICKI GLASNIK-TECHNICAL JOURNAL, 2024, 18 (02): : 282 - 288
  • [36] Digital twin process and simulation operation control technology for intelligent manufacturing unit
    He, Yichao
    Zhang, Niansong
    Wang, Aimin
    4TH INTERNATIONAL CONFERENCE ON RELIABILITY ENGINEERING (ICRE 2019), 2020, 836
  • [37] Automatic Generation of a Simulation-based Digital Twin of an Industrial Process Plant
    Martinez, Gerardo Santillan
    Sierla, Seppo
    Karhela, Tommi
    Vyatkin, Valeriy
    IECON 2018 - 44TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2018, : 3084 - 3089
  • [38] Simulation Optimization of Assembly Production Line Process Based on Digital Twinning
    Gao, Xingyu
    Long, Jialing
    ADVANCES IN MACHINERY, MATERIALS SCIENCE AND ENGINEERING APPLICATION, 2022, 24 : 473 - 478
  • [39] A modified Q-learning algorithm for robot path planning in a digital twin assembly system
    Xiaowei Guo
    Gongzhuang Peng
    Yingying Meng
    The International Journal of Advanced Manufacturing Technology, 2022, 119 : 3951 - 3961
  • [40] A modified Q-learning algorithm for robot path planning in a digital twin assembly system
    Guo, Xiaowei
    Peng, Gongzhuang
    Meng, Yingying
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2022, 119 (5-6): : 3951 - 3961