Abstract
Raise boring is an important method to construct underground shafts of mines and other underground infrastructures by drilling down the pilot hole and then reaming up to the desired diameter. As a typical cyber-physical system, the raise boring construction project is full of high heterogeneity, complexity and intrinsic uncertainty. Currently, its decision making loop is mainly based on the document-based system engineering and expertise experience. Regarding the intrinsic invisibility and uncertain risks in the underground engineering, especially for the remotely underground constructions on the extraterrestrial planets, it is absolutely required to shift the document-based and experience-dependent decision making paradigm into a digital and smart way. To this end, a systematic framework of the digital twin-driven process planning system for the raise boring method was conceived and presented. Then following the principles of open architecture, modularization and extensibility, a five-dimension architecture of digital twinning was built comprehensively that contained physical entity, digital representation, service entity, cross-systems entity and connection entity. Furthermore, a digital twin-driven decision making prototype system for the raise boring process was developed by the hybrid modeling of data-based model, visual geometric models, domain knowledge-based model and physics-based model. System verification indicated that the presented system had great potentials to facilitate the already very complicated process planning via the planning recommendation, visual simulation and models fusion. Finally, the contributions, novelty and limitations of this endeavour to extend the current digital twin practice were discussed.
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- \(N\) :
-
Rock breaking pressure
- \({F}_{thrust}\) :
-
Pulling force of reaming
- \({T}_{q}\) :
-
Working torque
- \({W}_{bit}\) :
-
Weight of reaming bit
- \({W}_{rods}\) :
-
Weight of drilling rods
- \(v\) :
-
Rate of penetration
- \(D\) :
-
Reaming diameter
- \(d\) :
-
Pilot hole diameter
- \(n\) :
-
Rotation speed of drilling
- \({a}_{z}\) :
-
Work ratio of rock breaking
- \({T}_{n}\) :
-
Teeth number of each turn on the rolling cutter
- \({E}_{h}\) :
-
Heat energy led by frictions
- \({f}_{s}\) :
-
Static friction coefficient
- \({f}_{d}\) :
-
Dynamic friction coefficient
- \(h\) :
-
Penetration depth
- \({h}_{1}, \,{h}_{2}, \text{and}\,{h}_{3}\) :
-
Instantaneous penetration depths of engaged cutter teeth
- \({F}_{n}\) :
-
Normal force of cutter teeth
- \({F}_{t}\) :
-
Tangential force of cutter teeth
- \({\sigma }_{c}\) :
-
Rock compressive strength
- \(\theta \) :
-
Rotation angle of rolling cutter
- \(RQD\) :
-
Rock quality designation
- \({C}_{n}\) :
-
Total turns of cutter teeth on the rolling cutter
- \(SSH\) :
-
Shore scleroscope hardness
- \({E}_{sta}\) :
-
Static elasticity modulus
- \( \sigma_{t}\) :
-
Brazilian tensile strength
- \(R\) :
-
Radius of rolling cutter
- \(r\) :
-
Radius of ball teeth
- AAS:
-
Asset administration shell
- AI:
-
Artificial intelligence
- CAPP:
-
Computer aided process planning
- CPS:
-
Cyber-physical system
- DBSE:
-
Document-based systems engineering
- DT:
-
Digital twin
- XML:
-
Extensible markup language
- ERP:
-
Enterprise resource planning
- 5D:
-
Five-dimension
- GUI:
-
Graphic user interface
- IIC:
-
Industrial internet consortium
- IoT:
-
Internet of things
- ISO:
-
International organization for standardization
- ML:
-
Machine learning
- OPC UA:
-
Open platform communications unified architecture
- PLM:
-
Product lifecycle management
- PLC:
-
Programmable logic controller
- RBM:
-
Raise boring machine
- SME:
-
Subject matter expertise
- 3D:
-
Three-dimensional
References
Aivaliotis, P., Georgoulias, K., Arkouli, Z., et al. (2019). Methodology for enabling digital twin using advanced physics-based modelling in predictive maintenance. Procedia Cirp, 81, 417–422. https://doi.org/10.1016/j.procir.2019.03.072
Arrichiello, V., & Gualeni, P. (2020). Systems engineering and digital twin: A vision for the future of cruise ships design, production and operations. International Journal on Interactive Design and Manufacturing, 14(1), 115–122.
Bergs, T., Gierlings, S., Auerbach, T., et al. (2021). The concept of digital twin and digital shadow in manufacturing. Procedia CIRP, 101, 81–84. https://doi.org/10.1016/j.procir.2021.02.010
Cavalieri, S., & Salafia, M. G. (2020). A model for predictive maintenance based on asset administration shell. Sensors, 20(21), 6028. https://doi.org/10.3390/s20216028
Cheng, S. Y., Gao, F., Jing, G. Y., et al. (2021). An intelligent temporary while-boring support technology for raise boring method. E3S Web of Conferences, 233(5), 01075. https://doi.org/10.1051/e3sconf/202123301075
Dröder, K., Bobka, P., Germann, T., et al. (2018). A machine learning-enhanced digital twin approach for human-robot-collaboration. Procedia Cirp, 76, 187–192. https://doi.org/10.1016/j.procir.2018.02.010
Ellgass, W., Holt, N., Saldana-Lemus, H., et al. (2018). A digital twin concept for manufacturing systems. In: Proceedings of the ASME 2018 International Mechanical Engineering Congress and Exposition. Volume 2: Advanced Manufacturing. Pittsburgh, Pennsylvania, USA. November 9–15, 2018, V002T02A073. https://doi.org/10.1115/IMECE2018-87737
Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In J. Kahlen, S. Flumerfelt, & A. Alves (Eds.), Transdisciplinary perspectives on complex systems. Springer, Cham. https://doi.org/10.1007/978-3-319-38756-7_4
Hu, F. (2022a). Mutual information-enhanced digital twin promotes vision-guided robotic grasping. Advanced Engineering Informatics, 52, 101562. https://doi.org/10.1016/j.aei.2022.101562
Hu, F. (2022b). Digital twin-driven reconfigurable fixturing optimization for trimming operation of aircraft skins. Aerospace, 9, 154. https://doi.org/10.3390/aerospace9030154
Industrial Internet Consortium. (2021). Digital Twins for Industrial Application, an Industrial Internet Consortium White Paper. Available online: https://www.iiconsortium.org/pdf/IIC_Digital_Twins_Industrial_Apps_White_Paper_2020-02-18.pdf (accessed on 27 July 2021).
Jacoby, M., Jovicic, B., Stojanovic, L., et al. (2021). An approach for realizing hybrid digital twins using asset administration shells and apache streampipes. Information, 12(6), 217. https://doi.org/10.3390/info12060217
Jing, G., Hu, F., & Chen, Y. (2020). Failure analysis and revamping methods for loading threaded joints of raiseboring machine. IOP Conference Series: Earth and Environmental Science. https://doi.org/10.1088/1755-1315/546/5/052064
Kalinowski, P., Długosz, O., & Kamiński, P. (2021). Digital twin of the mining shaft and hoisting system as an opportunity to improve the management processes of shaft infrastructure diagnostics and monitoring. In S. Shirowzhan (Ed.), Data science, data visualization, and digital twins. London: IntechOpen. https://doi.org/10.5772/intechopen.96193
Kang, J. S., Chung, K., & Hong, E. J. (2021). Multimedia knowledge-based bridge health monitoring using digital twin. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-021-10649-x
Kochhar, N. (2021). A digital twin for holistic autonomous vehicle development. Atzelectronics Worldwide, 16(3), 8–13. https://doi.org/10.1007/s38314-020-0579-2
Kritzinger, W., Karner, M., Traar, G., et al. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474
Lim, K. Y. H., Zheng, P., & Chen, C. H. (2020). A state-of-the-art survey of Digital Twin: Techniques, engineering product lifecycle management and business innovation perspectives. Journal of Intelligent Manufacturing, 31(6), 1313–1337. https://doi.org/10.1007/s10845-019-01512-w
Liu, M., Fang, S., Dong, H., et al. (2021). Review of digital twin about concepts, technologies, and industrial applications. Journal of Manufacturing Systems, 58, 346–361. https://doi.org/10.1016/j.jmsy.2020.06.017
Liu, Z., & Meng, Y. (2015). Key technologies of drilling process with raise boring method. Journal of Rock Mechanics and Geotechnical Engineering, 7(4), 385–394. https://doi.org/10.1016/j.jrmge.2014.12.006
Lu, Q., Parlikad, A. K., Woodall, P., et al. (2020). Developing a digital twin at building and city levels: Case study of West Cambridge campus. Journal of Management in Engineering, 36(3), 05020004. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000763
Luo, W., Hu, T., Ye, Y., et al. (2020). A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin. Robotics and Computer-Integrated Manufacturing, 65, 101974. https://doi.org/10.1016/j.rcim.2020.101974
Madni, A. M., Madni, C. C., & Lucero, S. D. (2019). Leveraging digital twin technology in model-based systems engineering. Systems, 7(1), 7. https://doi.org/10.3390/systems7010007
Marmolejo-Saucedo, J. A. (2020). Design and development of digital twins: A case study in supply chains. Mobile Networks and Applications, 25, 2141–2160. https://doi.org/10.1007/s11036-020-01557-9
Negri, E., Fumagalli, L., & Macchi, M. (2017). A review of the roles of digital twin in CPS-based production systems. Procedia Manufacturing, 11, 939–948. https://doi.org/10.1016/j.promfg.2017.07.198
Qi, Q., Tao, F., Zuo, Y., et al. (2018). Digital twin service towards smart manufacturing. Procedia Cirp, 72, 237–242. https://doi.org/10.1016/j.procir.2018.03.103
Rasheed, A., San, O., & Kvamsdal, T. (2020). Digital twin: Values, challenges and enablers from a modeling perspective. IEEE Access, 8, 21980–22012. https://doi.org/10.1109/ACCESS.2020.2970143
Redelinghuys, A. J. H., Basson, A. H., & Kruger, K. (2019). A six-layer architecture for the digital twin: A manufacturing case study implementation. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-019-01516-6
Schrotter, G., & Hürzeler, C. (2020). The digital twin of the city of Zurich for urban planning. PFG-Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 88(1), 99–112. https://doi.org/10.1007/s41064-020-00092-2
Semeraro, C., Lezoche, M., Panetto, H., et al. (2021). Digital twin paradigm: A systematic literature review. Computers in Industry, 130, 103469. https://doi.org/10.1016/j.compind.2021.103469
Sepasgozar, S. M. E. (2021). Differentiating digital twin from digital shadow: Elucidating a paradigm shift to expedite a smart, sustainable built environment. Buildings, 11(4), 151. https://doi.org/10.3390/buildings11040151
Shaterpour-Mamaghani, A., & Copur, H. (2021). Empirical performance prediction for raise boring machines based on rock properties, pilot hole drilling data and raise inclination. Rock Mechanics and Rock Engineering, 54(4), 1707–1730. https://doi.org/10.1007/s00603-020-02355-1
Shaterpour-Mamaghani, A., Copur, H., Dogan, E., et al. (2018). Development of new empirical models for performance estimation of a raise boring machine. Tunnelling and Underground Space Technology, 82, 428–441. https://doi.org/10.1016/j.tust.2018.08.056
Sjarov, M., Lechler, T., Fuchs, J., et al. (2020). The Digital Twin Concept in Industry-A Review and Systematization. In: Proceedings of the 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE, Vienna, Austria, 8–11 Sept. 2020, vol 1, pp 1789–1796. https://doi.org/10.1109/ETFA46521.2020.9212089
Subramanian, K. (2020). Digital twin for drug discovery and development—The virtual liver. Journal of the Indian Institute of Science. https://doi.org/10.1007/s41745-020-00185-2
Ullah, A. M. M. S. (2019). Modeling and simulation of complex manufacturing phenomena using sensor signals from the perspective of Industry 4.0. Advanced Engineering Informatics, 39, 1–13. https://doi.org/10.1016/j.aei.2018.11.003
Wang, H., Li, H., Wen, X., et al. (2021). Unified modeling for digital twin of a knowledge-based system design. Robotics and Computer-Integrated Manufacturing, 68, 102074. https://doi.org/10.1016/j.rcim.2020.102074
Wang, Q., Jiao, W., Wang, P., et al. (2020). Digital twin for human-robot interactive welding and welder behavior analysis. IEEE/CAA Journal of Automatica Sinica, 8(2), 334–343. https://doi.org/10.1109/JAS.2020.1003518
Xiong, M., Wang, H., Fu, Q., et al. (2021). Digital twin-driven aero-engine intelligent predictive maintenance. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-021-06976-w
Xu, B., Wang, J., Wang, X., et al. (2019). A case study of digital-twin-modelling analysis on power-plant-performance optimizations. Clean Energy, 3(3), 227–234. https://doi.org/10.1093/ce/zkz025
Yarim, G., Ritchie, G. M., & May, R. B. (2010). A guide to successful backreaming: Real-time case histories. SPE Drilling & Completion, 25(01), 27–38. https://doi.org/10.2118/116555-PA
Zhang, C., Xu, W., Liu, J., et al. (2019). Digital twin-enabled reconfigurable modeling for smart manufacturing systems. International Journal of Computer Integrated Manufacturing. https://doi.org/10.1080/0951192X.2019.1699256
Zhang, L., Zhou, L., & Horn, B. K. P. (2021). Building a right digital twin with model engineering. Journal of Manufacturing Systems, 59, 151–164. https://doi.org/10.1016/j.jmsy.2021.02.009
Zhang, X., & Zhu, W. (2019). Application framework of digital twin-driven product smart manufacturing system: A case study of aeroengine blade manufacturing. International Journal of Advanced Robotic Systems, 16(5), 1729881419880663. https://doi.org/10.1177/1729881419880663
Zhou, M., Yan, J., & Feng, D. (2019). Digital twin framework and its application to power grid online analysis. CSEE Journal of Power and Energy Systems, 5(3), 391–398. https://doi.org/10.17775/CSEEJPES.2018.01460
Acknowledgements
The authors would like to acknowledge the financial support of the cooperation project between industry and universities approved by the Ministry of Education of China (Grant No. 202002148005), and the National Key Research and Development Program of China (Grant No. 2016YFC0600802).
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Hu, F., Qiu, X., Jing, G. et al. Digital twin-based decision making paradigm of raise boring method. J Intell Manuf 34, 2387–2405 (2023). https://doi.org/10.1007/s10845-022-01941-0
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DOI: https://doi.org/10.1007/s10845-022-01941-0