Digital twin for CNC machine tool: modeling and using strategy
By Weichao Luo, Tianliang Hu, Chengrui Zhang, Yongli Wei. Journal of Ambient Intelligence and Humanized Computing, 2018
298 citations recorded by the source corpus.
Subfields: Industrial and Manufacturing Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/38316c7536fcfa5b73b1445e98c7ca6a3831c40f
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In Industrial and Manufacturing Engineering
In Mechanical Engineering
Citations in the corpus
- A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics (2021, background): “, tools [116,117], bearings [118], ball screws [119], gears [15,120,121], pumps [122]) and the energy efficiency of the equipment [123,124] in order to handle the conflict between the unplanned maintenance operations and the resulting costs and productivity, particularly in SMEs, due to their limite”
- Characterising the Digital Twin: A systematic literature review (2020, unknown): “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]”
- Differentiating Digital Twin from Digital Shadow: Elucidating a Paradigm Shift to Expedite a Smart, Sustainable Built Environment (2021, background): “The key characteristics of DT as mentioned in the definitions are: fidelity [74,75], real-time control [76], real-time optimisation, virtual replica [60,77], dynamic digital replica [78], and dynamic bi-directional [35,79]. real-time optimisation, virtual replica [60,77], dynamic digital replica…”