Digital twin-driven product design framework

By Fei Tao, Fangyuan Sui, Ang Liu, Qinglin Qi, Meng Zhang, Boyang Song, Zirong Guo, Stephen C. Y. Lu, Andrew Yeh-Ching Nee. International Journal of Production Research, 2019

1,033 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/6a3ec5bd0bc66f252a04b7afacc5a29b727f69c9

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In Industrial and Manufacturing Engineering

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Citations in the corpus

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).”
A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives (2019, background): “…physical entity in the real world and can simulate its physical counterpart’s characteristics, behavior, life and performance in a timely fashion” Qi and Tao (2018) “Virtual models of physical objects are created in a digital way to simulate their behaviors in real-world environments” Xu et…”
Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison (2019, background): “Recently, DTs have also been adopted in various industries for purposes including product design [20], production line design [21], DT shop ﬂoors [22], production process optimization [23], and prognosis and health management [24].”
Digital Twin in Industry: State-of-the-Art (2019, methodology): “A case study on bicycle design was conducted to instantiate the framework [35].”
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]”
Digital Twin for rotating machinery fault diagnosis in smart manufacturing (2018, methodology): “Digital Twin not only can be used for modelling and simulation of system development to support design or to validate system properties (Schleich et al. 2017; Tao et al. 2018) but also can support the operation and manufacturing service for optimised operations and failure prediction.”
A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics (2021, background): “, product design [12,13], modeling and simulation [14], fault diagnostics and prognostics [15].”
Construction with digital twin information systems (2020, background): “According to Tao et al. (2019a), digital twins have three main elements: a physical artefact, a digital counterpart, and the connection that binds the two together.”
Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning (2021, background): “…and simulation, advanced sensing and signal processing, cloud and edge computing, and 5G, the concept of digital twin (DT) has gained increasing attention in recent years in various areas of smart manufacturing such as product design [20], job shop scheduling [21], and process optimization [22].”
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, background): “the data lifecycle in manufacturing, in [40] and [41] the authors highlighted the role of big data in supporting smart manufacturing.”
The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities (2021, background): “Similarly, Tao et al. [19] considered the aspect of product life cycle and interpreted the digital twin as ‘‘a real mapping of all components in the product life cycle using physical data, virtual data and interaction data between them.’’”