Digital Twin Service towards Smart Manufacturing

By Qinglin Qi, Fei Tao, Ying Zuo, Dongming Zhao. 2018

404 citations recorded by the source corpus.

Abstract Digital twin provides an effective way for the cyber-physical integration of manufacturing. Meanwhile, smart manufacturing services could optimize the entire business processes and operation procedure of manufacturing, to achieve a new higher level of productivity. The combination of smart manufacturing services and digital twin would radically change product design, manufacturing, usage, MRO and other processes. Combined with the services, the digital twin will generate more reasonable manufacturing planning and precise production control to help achieve smart manufacturing, through the two-way connectivity between the virtual and physical worlds of manufacturing. This paper specifies and highlights how manufacturing services and digital twin are converged together and the various components of digital twin are used by manufacturers in the form of services.

Subfields: Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/8e10d3a24a8d365ee7edac9b1c893cd135e58646

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Industrial and Manufacturing Engineering

Citations in the corpus

Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models (2020, methodology): “In [184], an analysis of some current industrial initiative is provided as well as a general overview of the “servitization of the DT.”
Digital Twin: Enabling Technologies, Challenges and Open Research (2020, background): “AI and the following topics below show the growth of advanced data analytics [64], [65].”
Digital twin paradigm: A systematic literature review (2021, background): “, energy monitoring and fault monitoring) (Qi et al., 2018).”
Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison (2019, methodology): “However, the virtual models of a system-level DT must be formed through the integration and collaboration of multiple unit-level models [48].”
Characterising the Digital Twin: A systematic literature review (2020, methodology): “5 Fidelity [33] [80] [108] [24] [85] [8] [56] [88] [68] [112] [99] [78] [32] [111] [36] [7] [71] [63] [61] [73] [20] [67] [6] [4] [48] [55] [95] [16] [92] [98] [87] [9] [66] [110] [18] [89] [12]”
A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, background): “Qi and Tao are the the most highly-cited researchers in this field at this time (Tao et al. 2018; Qi and Tao 2018; Tao and Zhang 2017; Qi et al. 2018).”
Digital Twin: Vision, Benefits, Boundaries, and Creation for Buildings (2019, background): “enhancement of building operation and maintenance and for the implementation of a closed-loop design [23].”
Introducing digital twins to agriculture (2021, methodology): “DT are utilized by several leading companies and organizations, including Siemens (Negri et al., 2017), General Electric, NASA, US Airforce (Mukherjee and DebRoy, 2019), Oracle, ANSYS, SAP, and Altair (Qi et al., 2018).”