Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison

By Qinglin Qi, Fei Tao. IEEE Access, 2018

1,314 citations recorded by the source corpus.

With the advances in new-generation information technologies, especially big data and digital twin, smart manufacturing is becoming the focus of global manufacturing transformation and upgrading. Intelligence comes from data. Integrated analysis for the manufacturing big data is beneficial to all aspects of manufacturing. Besides, the digital twin paves a way for the cyber-physical integration of manufacturing, which is an important bottleneck to achieve smart manufacturing. In this paper, the big data and digital twin in manufacturing are reviewed, including their concept as well as their applications in product design, production planning, manufacturing, and predictive maintenance. On this basis, the similarities and differences between big data and digital twin are compared from the general and data perspectives. Since the big data and digital twin can be complementary, how they can be integrated to promote smart manufacturing are discussed.

Subfields: Industrial and Manufacturing Engineering, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/80dd97954ddf3edd22d4cb21f0ac31b7ffed6bbf

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Citations in the corpus

Digital Twin: Vision, Benefits, Boundaries, and Creation for Buildings (2019, background): “Digital twin works specifically with real-time data fed by the sensor systems to record and analyze the real-time structural and environmental parameters of a physical asset for the purpose of performing highly accurate digital twin simulation and data analytics [26].”
Digital Twin: Enabling Technologies, Challenges and Open Research (2020, methodology): “It gives a comparison of enabling technologies for Digital Twins as well as arguing the importance of emerging technologies for the development of smart manufacturing [25].”
Digital Twin in Industry: State-of-the-Art (2019, background): “to improve the performance of product/process in the physical space [2].”
Characterising the Digital Twin: A systematic literature review (2020, neutral): “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]”
All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda (2021, background): “, structured data, unstructured data, and semi-structured data, which makes deep learningdriven digital twin essential [252].”
A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications (2019, background): “…fact that a continuous interaction, convergence, and self-adaptation of the DT are of great importance for guaranteeing a full synchronization between the DT and its physical twin, which is necessary to obtain consistent monitoring, optimizations, predictive maintenance processes, and so on [56].”
Industrial applications of digital twins (2021, background): “In review-type articles [3,18,23,25] and references therein.”
The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review (2020, methodology): “Digital Twin (DT) is also important in NPD as it replicates the digital representation of physical products for iterative optimisation of personalised design (Qi and Tao 2018).”
Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing (2019, background): “Qi and Tao (2018) introduced a serial digital twin-driven frameworks for smart manufacturing, including digital twin-driven design (Tao, Sui, et al. 2018), manufacturing (Tao and Zhang 2017), service (Tao, Cheng, et al. 2018) and prognostics and health management (Tao, Zhang, et al. 2018).”
Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions (2020, methodology): “Despite being domain-dependent, there are issues regarding data in DT which exist across domains: Qi and Tao [57] compare and contrast Big Data and DT, within the context of smart manufacturing & Industry 4.0.”