DIGITAL TWIN OF SPINDLE UNITS. REVIEW
DOI:
https://doi.org/10.35546/kntu2078-4481.2023.4.17Keywords:
intelligent spindle, DT, industry 4.0, model of digital twins, metal cutting machinery.Abstract
The Digital Twin technology of the spindle unit can realize the control of accuracy, quality of processing and other indicators that affect to the efficiency of the system. Many articles focus on Digital Twin, but there are no clear and systematic analysis of the process of creating Digital Twin. To address this gap, this study conducted an article analysis of the Digital Twin of the spindle unit (up to Maу 1, 2023). In this article, we will consider the existing definitions of the concepts of digital twin and digital twin of metalcutting machines. In addition to the articles themselves, we will consider other reviews of digital twins and attempts to systematize existing research. No review articles on the topic of digital twins of spindle assemblies were found. For the search methodology, traditional methods of searching scientific sites and search engines were used, and the analysis of the obtained results is presented in graphs. The analysis included a total of 143 selected publications. After a detailed examination of which, it turned out that most of the articles, despite the presence of keywords, did not deal with the creation of digital twins, but only mentioned the possibility of creating digital twins. Some articles have ideas for creating a digital twin model for metal cutting machinery or spindle unit. As a result of the search, no domestic works in the given direction were found, most of the works are foreign, of which the lion’s share is financed by the government of the People’s Republic of China. It is expected that this will give an impetus to the in-depth study of the process of creating a digital twin of a spindle node. It can be useful in the post-war reconstruction of Ukraine, taking into account the problems of wear and tear of existing machine tools of machine-building enterprises. In addition, it is a possibility of adapting machines that were in use, which can be obtained in within the framework of international assistance to enterprises.
References
Grieves, M. (2014). Digital Twin: Manufacturing Excellence Through Virtual Factory Replication. Whitepaper. https://doi.org/10.5281/zenodo.1493930
Lim, K.Y.H., Zheng, P. & Chen, C. A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives. Journal of Intelligent Manufacturing 31, 1313–1337 (2020). https://doi.org/10.1007/s10845-019-01512-w
Stark, R., Kind, S., & Neumeyer, S. (2017). Innovations in digital modelling for next generation manufacturing system design. CIRP Annals – Manufacturing Technology, 66(1), 169–172. https://doi.org/10.1016/j.cirp.2017.04.045
Söderberg, R., Wärmefjord, K., Carlson, J. S., & Lindkvist, L. (2017). Toward a Digital Twin for real-time geometry assurance in individualized production. CIRP Annals – Manufacturing Technology, 66(1), 137–140. https://doi.org/10.1016/j.cirp.2017.04.038
Zhuang, C., Liu, J., &Xiong, H. (2018). Digital twin-based smart production management and control framework for the complex product assembly shop-floor. International Journal of 32 Advanced Manufacturing Technology, 96(1–4), 1149–1163. https://doi.org/10.1007/s00170-018-1617-6
Qi, Q., & Tao, F. (2018). Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison. IEEE Access, 6, 3585–3593. https://doi.org/10.1109/ACCESS.2018.2793265
Xu, Y., Sun, Y., Liu, X., & Zheng, Y. (2019). A Digital-Twin-Assisted Fault Diagnosis using Deep Transfer Learning. IEEE Access, 7, 1–1. https://doi.org/10.1109/access.2018.2890566
Kannan, K., &Arunachalam, N. (2019). A Digital Twin for Grinding Wheel: An Information Sharing Platform for Sustainable Grinding Process. Journal of Manufacturing Science and Engineering, 141(2), 021015. https://doi.org/10.1115/1.4042076
Ward, R., Sun, C., Dominguez-Caballero, J. et al. Machining Digital Twin using real-time model-based simulations and look ahead function for closed loop machining control. International Journal Advanced Manufacturing Technology 117, 3615–3629 (2021). https://doi.org/10.1007/s00170-021-07867-w
Liu S., Bao J., PaiZ. (2023). A review of digital twin-driven machining: From digitization to intellectualization. Journal of Manufacturing Systems. https://doi.org/10.1016/j.jmsy.2023.02.010.
Fujita Tomoya, XiTiandong, Ikeda Ryosuke, Kehne Sebastian, Fey Marcel, Brecher Christian. (2022). Identification of a Practical Digital Twin for Simulation of Machine Tools. International Journal of Automation Technology. 16. 261-268. https://doi.org/10.20965/ijat.2022.p0261.
Wu, L.; Leng, J.; Ju, B. Digital Twins-Based Smart Design and Control of Ultra-Precision Machining: A Review. Symmetry 2021, 13, 1717. https://doi.org/10.3390/sym13091717
Cao, H., Zhang, X., Chen, X. (2017) The concept and progress of intelligent spindles: a review. International Journal of Machine Tools & Manufacture, (112), 21–52. https://doi.org/10.1016/j.ijmachtools.2016.10.005
Lu, Y., Liu, C., Kevin, I., Wang, K., Huang, H., & Xu, X. (2020). Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics and Computer-Integrated Manufacturing, 61, 101837. https://doi.org/10.1016/j.rcim.2019.101837
Luca Lattanzi, Roberto Raffaeli, Margherita Peruzzini& Marcello Pellicciari (2021) Digital twin for smart manufacturing: a review of concepts towards a practical industrial implementation, International Journal of Computer Integrated Manufacturing, 34:6, 567–597, https://doi.org/10.1080/0951192X.2021.1911003
Scopus.com. URL: https://www.scopus.com/(avaible 26.10.2023).
Science direct. URL: https://www.sciencedirect.com/ (available 26.10.2023).
Web of science. URL: https://www.webofscience.com/ (available 26.10.2023).
МDPI. URL: https://www.mdpi.com/ (available 26.10.2023).
Reseаrch Gate. URL: https://www.researchgate.net/ (available 26.10.2023).
Google Scholar. URL: https://scholar.google.com/ (available 26.10.2023).
Vosviewer. URL: https://www.vosviewer.com/(available 03.10.2023).
Grieves, M., Vickers, J., 2017, Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. Trans disciplinary perspective son complex systems, 85–113.
Stark, J., 2015, Product lifecycle management. Product Lifecycle Management (vol 1), 1–29.
Wójcicki, J., Leonesio, M.P., & Bianchi, G. (2021). Potential for smart spindles adoption as edge computing nodes in Industry 4.0. Procedia CIRP, (99), 86–91. DOI:10.1016/j.procir.2021.03.015
Wójcicki, J., & Bianchi, G. (2020). A smart spindle component concept as a standalone measurement system for Industry 4.0 machine tools. 2020.IEEE International Workshop on Metrology for Industry 4.0 &IoT (рр. 278–282). DOI: 10.1109/MetroInd4.0IoT48571.2020.9138280
Scaglioni, B.; Ferretti, G. Towards digital twins through object-oriented modelling: A machine tool case study. IFAC Pap. 2018, 51, 613–618. https://doi.org/10.1016/j.ifacol.2018.03.104
Christiand, Gandjar Kiswanto, Digital Twin Approach for Tool Wear Monitoring of Micro-Milling, Procedia CIRP, Volume 93, 2020, 1532–1537, ISSN 2212-8271, https://doi.org/10.1016/j.procir.2020.03.140.
Lu, Q.; Zhu, D.; Wang, M.; Li, M. Digital Twin-Driven Thermal Error Prediction for CNC Machine Tool Spindle. Lubricants 2023, 11, 219. https://doi.org/10.3390/lubricants11050219
Hänel А., Schnellhardt Т., Wenkler Е., Nestler А., Brosius А., Corinth С., Fay А., Ihlenfeldt S., The development of a digital twin for machining processes for the application in aerospace industry, Procedia CIRP, Volume 93, 2020, 1399-1404, ISSN 2212-8271 https://doi.org/10.1016/j.procir.2020.04.017.
Zhang L, Xuan J, Shi T, etal (2020) Robust, fractal theory, and FEM-based temperature field analysis for machine tool spindle [J]. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-020-05926-2
Xiao, J., Fan, K. Research on the digital twin for thermal characteristics of motorized spindle. The International Journal of Advanced Manufacturing Technology 119, 5107–5118 (2022). https://doi.org/10.1007/s00170-021-08508-y
Davies О., Makkattil A., Ce Jiang, FarsiМ.,A Digital Twin Design for Maintenance Optimization, Procedia CIRP, Volume 109,2022,Pages 395-400, ISSN 2212-8271. https://doi.org/10.1016/j.procir.2022.05.268.
V.S. Vishnu, Kiran George Varghese, B. Gurumoorthy, A Data-driven Digital Twin of CNC Machining Processes for Predicting Surface Roughness, Procedia CIRP, Volume 104,2021, Pages 1065-1070, ISSN 2212-8271, https://doi.org/10.1016/j.procir.2021.11.179
Xie N., Kou R., Yao Y.,Tool Condition Prognostic Model Based on Digital Twin System,Procedia CIRP,Volume 93,2020,Pages 1502-1507,ISSN 2212-8271, https://doi.org/10.1016/j.procir.2020.03.045.
J. Liu, D. Yu, Y. Hu, H. Yu, W. He and L. Zhang, “CNC Machine Tool Fault Diagnosis Integrated Rescheduling Approach Supported by Digital Twin-Driven Interaction and Cooperation Framework,” in IEEE Access, vol. 9, pp. 118801-118814, 2021, https://doi:10.1109/ACCESS.2021.3106797.
Dai, Ye & Pang, Jian &Rui, XuKun& Li, WeiWei& Wang, QingHai& Li, ShiKun. (2023). Thermal error prediction model of high-speed motorized spindle based on DELM network optimized by weighted mean of vectors algorithm. Case Studies in Thermal Engineering. 47. 103054. https://doi.org/10.1016/j.csite.2023.103054.
Armendia M., Cugnon F., Berglind L., OzturkE., Gil G., Selmi J.,Evaluation of Machine Tool Digital Twin for machining operations in industrial environment,Procedia CIRP,Volume 82,2019, p. 231-236, ISSN 2212-8271. https://doi.org/10.1016/j.procir.2019.04.040.