Attaran Mohsen, Attaran Sharmin, Celik Bilge Gokhan
School of Business and Public Administration, California State University, Bakersfield, 9001 Stockdale Highway, Bakersfield, CA 93311-1099 USA.
Bryant University, 1150 Douglas Pike, Smithfield, RI 02917 USA.
Adv Comput Intell. 2023;3(3):11. doi: 10.1007/s43674-023-00058-y. Epub 2023 Jun 7.
As the adoption of Industry 4.0 advances and the manufacturing process becomes increasingly digital, the Digital Twin (DT) will prove invaluable for testing and simulating new parameters and design variants. DT solutions build a 3D digital replica of the physical object allowing the managers to develop better products, detect physical issues sooner, and predict outcomes more accurately. In the past few years, Digital Twins (DTs) dramatically reduced the cost of developing new manufacturing approaches, improved efficiency, reduced waste, and minimized batch-to-batch variability. This paper aims to highlight the evolution of DTs, review its enabling technologies, identify challenges and opportunities for implementing DT in Industry 4.0, and examine its range of applications in manufacturing, including smart logistics and supply chain management. The paper also highlights some real examples of the application of DT in manufacturing.
随着工业4.0的推进以及制造过程日益数字化,数字孪生(DT)对于测试和模拟新参数及设计变体将证明具有极高价值。DT解决方案构建物理对象的3D数字复制品,使管理者能够开发出更好的产品,更快地检测出物理问题,并更准确地预测结果。在过去几年中,数字孪生(DT)显著降低了开发新制造方法的成本,提高了效率,减少了浪费,并将批次间的可变性降至最低。本文旨在突出数字孪生的发展历程,回顾其使能技术,确定在工业4.0中实施DT的挑战与机遇,并研究其在制造领域的应用范围,包括智能物流和供应链管理。本文还重点介绍了DT在制造领域应用的一些实际案例。