Abstract
With the rapid development of aerospace, the large complex curved workpiece is widely used. However, the lack of digital monitoring and detection in the current manufacturing process leads to the low efficiency of the parts produced and processed, and quality consistency cannot be guaranteed. Aiming at the problems of low degree of virtual visualization and insufficient monitoring ability of large complex surface machining, a framework of large complex surface machining monitoring system based on digital twin technology was proposed. The digital research of intelligent processing monitoring system is carried out from six dimensions. By studying the key technologies of virtual twin model construction, multi-source data acquisition and transmission, and virtual-real mapping relationship construction, a digital twin monitoring system for large complex surface machining is developed. Finally, the feasibility and effectiveness of the twin system are verified by a real scene, and it provides a reference for monitoring the machining process of large complex curved workpieces.














Similar content being viewed by others
References
AboElHassan, A., & Yacout, S. (2022). A digital shadow framework using distributed system concepts. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-022-02028-6
Chen, Z., Fang, H., & Fang, Y. (2014). Machining method for coating on huge revolution surfaces. China Mechanical Engineering, 25(23), 3195–3199.
Cimino, C., Negri, E., & Fumagalli, L. (2019). Review of digital twin applications in manufacturing. Computers in Industry, 113, 103130. https://doi.org/10.1016/j.compind.2019.103130
Coronado, P. D. U., Lynn, R., Louhichi, W., Parto, M., Wescoat, E., & Kurfess, T. (2018). Part data integration in the shop floor digital Twin: mobile and cloud technologies to enable a manufacturing execution system. Journal of Manufacturing Systems, 48, 25–33. https://doi.org/10.1016/j.jmsy.2018.02.002
Duan, J. G., Ma, T. Y., Zhang, Q. L., Liu, Z., & Qin, J. Y. (2021). Design and application of digital twin system for the blade-rotor test rig. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01824-w
Eguia, J., Uriarte, L., & Lamikiz, A. (2016). Analysis, optimization and accuracy assessment of special-purpose portable machines by virtual techniques. International Journal of Machine Tools and Manufacture, 111, 31–42. https://doi.org/10.1016/j.ijmachtools.2016.09.006
Fan, Y., Yang, J., Chen, J., Hu, P., Wang, X., Xu, J., & Zhou, B. (2021). A digital-twin visualized architecture for flexible manufacturing system. Journal of Manufacturing Systems, 60, 176–201. https://doi.org/10.1016/j.jmsy.2021.05.010
Garg, G., Kuts, V., & Anbarjafari, G. (2021). Digital twin for fanuc robots: Industrial robot programming and simulation using virtual reality. Sustainability, 13(18), 10336. https://doi.org/10.3390/su131810336
Garland, M., & Heckbert, P. S. (1997). Surface simplification using quadric error metrics. IEEE Computer Graphics and Applications., 16, 64. https://doi.org/10.1109/38.491187
Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. Transdisciplinary Perspectives on Complex Systems. https://doi.org/10.1007/978-3-319-38756-7_4
Guo, Y., Sun, Y., & Wu, K. (2020). Research and development of monitoring system and data monitoring system and data acquisition of CNC machine tool in intelligent manufacturing. International Journal of Advanced Robotic Systems, 17(2), 1729881419898017. https://doi.org/10.1177/1729881419898017
Hüffner, F., Komusiewicz, C., Moser, H., & Niedermeier, R. (2010). Fixed-parameter algorithms for cluster vertex deletion. Theory of Computing Systems, 47(1), 196–217. https://doi.org/10.1007/s00224-008-9150-x
Kalvin, A. D., & Taylor, R. H. (1996). Superfaces: Polygonal mesh simplification with bounded error. IEEE Computer Graphics and Applications, 16(3), 64–77. https://doi.org/10.1109/38.491187
Lee, J., Lapira, E., Bagheri, B., & Kao, H. A. (2013). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, 1(1), 38–41. https://doi.org/10.1016/j.mfglet.2013.09.005
Lee, R. S., & Lin, Y. H. (2010). Development of universal environment for constructing 5-axis virtual machine tool based on modified D-H notation and OpenGL. Robotics and Computer-Integrated Manufacturing, 26(3), 253–262. https://doi.org/10.1016/j.rcim.2009.11.001
Liu, C., Hong, X., Zhu, Z., & Xu, X. (2018). Machine tool digital twin: Modelling methodology and applications. ORCA.
Liu, S., Lu, S., Li, J., Sun, X., Lu, Y., & Bao, J. (2021). Machining process-oriented monitoring method based on digital twin via augmented reality. The International Journal of Advanced Manufacturing Technology, 113(11), 3491–3508. https://doi.org/10.1007/s00170-021-06838-5
Liu, W., Kong, C., Niu, Q., Jiang, J., & Zhou, X. (2020). A method of NC machine tools intelligent monitoring system in smart factories. Robotics and Computer-Integrated Manufacturing, 61, 101842. https://doi.org/10.1016/j.rcim.2019.101842
Michael, G. (2022). Product lifecycle management.
Rossignac, J., & Borrel, P. (1993). Multi-resolution 3D approximations for rendering complex scenes. Modeling in computer graphics (pp. 455–465). Springer.
Syafrudin, M., Alfian, G., Fitriyani, N. L., & Rhee, J. (2018). Performance analysis of IoT-based sensor, big data processing, and machine learning model for real-time monitoring system in automotive manufacturing. Sensors, 18(9), 2946. https://doi.org/10.3390/s18092946
Tao, F., Qi, Q., Wang, L., & Nee, A. Y. C. (2019a). Digital twins and cyber–physical systems toward smart manufacturing and industry 4.0: Correlation and comparison. Engineering, 5(4), 653–661. https://doi.org/10.1016/j.eng.2019.01.014
Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Guo, Z., Lu, S. C. Y., & Nee, A. Y. (2019b). Digital twin-driven product design framework. International Journal of Production Research, 57(12), 3935–3953. https://doi.org/10.1080/00207543.2018.1443229
Tao, F., Zhang, H., Liu, A., & Nee, A. Y. (2018). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415. https://doi.org/10.1109/TII.2018.2873186
Vichare, P., Zhang, X., Dhokia, V., Cheung, W. M., Xiao, W., & Zheng, L. (2018). Computer numerical control machine tool information reusability within virtual machining systems. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 232(4), 593–604. https://doi.org/10.1177/0954405417708219
Wang, G., Cao, Y., & Zhang, Y. (2022). Digital twin-driven clamping force control for thin-walled parts. Advanced Engineering Informatics, 51, 101468. https://doi.org/10.1016/j.aei.2021.101468
Wang, W., Zhang, X., Li, Y., & Li, Y. (2016). Open CNC machine tool’s state data acquisition and application based on OPC specification. Procedia CIRP, 56, 384–388. https://doi.org/10.1016/j.procir.2016.10.061
Wang, Y., Zheng, L., & Wang, Y. (2021). Event-driven tool condition monitoring methodology considering tool life prediction based on industrial internet. Journal of Manufacturing Systems, 58, 205–222. https://doi.org/10.1016/j.jmsy.2020.11.019
Wenna, W., Weili, D., Changchun, H., Heng, Z., Haibing, F., & Yao, Y. (2022). A digital twin for 3D path planning of large-span curved-arm gantry robot. Robotics and Computer-Integrated Manufacturing, 76, 102330. https://doi.org/10.1016/j.rcim.2022.102330
Ye, Y., Hu, T., Zhang, C., & Luo, W. (2018). Design and development of a CNC machining process knowledge base using cloud technology. The International Journal of Advanced Manufacturing Technology, 94(9), 3413–3425. https://doi.org/10.1007/s00170-016-9338-1
Yu-Shun, W., Ling-Song, H., Gao, Z. Q., Jun-Feng, W., & Yang-Fan, C. (2019). Remote monitoring for the operation status of CNC machine tools based on HTML5. Advances in Technology Innovation, 4(4), 260–268.
Zhang, Y., Zhang, C., Yan, J., Yang, C., & Liu, Z. (2022). Rapid construction method of equipment model for discrete manufacturing digital twin workshop system. Robotics and Computer-Integrated Manufacturing, 75, 102309. https://doi.org/10.1016/j.rcim.2021.102309
Zhu, K., & Zhang, Y. (2018). A cyber-physical production system framework of smart CNC machining monitoring system. IEEE/ASME Transactions on Mechatronics, 23(6), 2579–2586. https://doi.org/10.1109/TMECH.2018.2834622
Zhu, L., Li, H., Liang, W., & Wang, W. (2015). A web-based virtual CNC turn-milling system. The International Journal of Advanced Manufacturing Technology, 78(1), 99–113. https://doi.org/10.1007/s00170-014-6649-y
Acknowledgements
The authors gratefully acknowledge the financial support of the Fundamental Research Funds for the Central Universities under Grant no. 2018JBZ007.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
All authors declare that they have no conflicts of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Qi, TF., Fang, HR., Chen, YF. et al. Research on digital twin monitoring system for large complex surface machining. J Intell Manuf 35, 977–990 (2024). https://doi.org/10.1007/s10845-022-02072-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-022-02072-2