Abstract
At automotive manufacturing sites, meeting the delivery schedule is difficult owing to the occurrence of unpredictable abnormal scenarios such as product defects and equipment failures. To overcome this, manufacturing technologies developed as part of the Fourth Industrial Revolution are employed to meet the delivery schedule set by the customer. We propose a digital twin (DT)–based cyber-physical system (CPS) that can predict whether a product can be manufactured as per the schedule requested by a customer at an automotive body production line where abnormal scenarios occur. We designed a product, process, plan, plant, and resource information model for automotive body production lines; the proposed DT employs this model. Unlike in previous research on DTs focusing on independent engineering application development, we designed and implemented a CPS combined with a DT and other components for a Web-based integrated manufacturing platform. To the best of our knowledge, this is the first time a DT-based CPS is implemented for abnormal scenarios involving automotive body production lines; the capability of the proposed system was verified via experiments. The experimental results indicate that the proposed system achieved an average prediction performance of 96.83% for the actual production plan. We confirmed that the DT-based CPS can be applied to automotive body production lines, and it provides an advanced solution to predict whether production is possible according to the production plan.
Similar content being viewed by others
Data availability
The datasets generated and/or analyzed during the current study are not publicly available due to corporate security and we cannot disclose it.
Code availability
Not applicable
Change history
14 May 2021
Springer Nature’s version of this paper was updated to present the correct Figure 1 caption.
References
Panetto H, Iung B, Ivanov D, Weichhart G, Wang X (2019) Challenges for the cyber-physical manufacturing enterprises of the future. Annu Rev Control 47:200–213. https://doi.org/10.1016/j.arcontrol.2019.02.002
Wang L, Törngren M, Onori M (2015) Current status and advancement of cyber-physical systems in manufacturing. J Manuf Syst 37:517–527. https://doi.org/10.1016/j.jmsy.2015.04.008
Grieves M (2014) Digital twin: manufacturing excellence through virtual factory replication. White paper 1:1-7. www.apriso.com/library/Whitepaper_Dr_Grieves_DigitalTwin_ManufacturingExcellence.php
Merkle L, Segura AS, Grummel JT, Lienkamp M (2019) Architecture of a digital twin for enabling digital services for battery systems. IEEE Int Conf Ind Cyber Phys Syst 2019:155–160. https://doi.org/10.1109/icphys.2019.8780347
Monostori L, Kádár B, Bauernhansl T, Kondoh S, Kumara S, Reinhart G, Sauer O, Schuh G, Sihn W, Ueda K (2016) Cyber-physical systems in manufacturing. CIRP Ann 65(2):621–641. https://doi.org/10.1016/j.cirp.2016.06.005
Lee J, Bagheri B, Kao HA (2015) A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf Lett 3:18–23. https://doi.org/10.1016/j.mfglet.2014.12.001
Klampfl E, Gusikhin O, Rossi G (2005) Optimization of workcell layouts in a mixed-model assembly line environment. Int J Flex Manuf Syst 17:277–299. https://doi.org/10.1007/s10696-006-9029-6
Agnetis A, Pacifici A, Rossi F, Lucertini M, Nicoletti S, Nicolò F, Oriolo G, Pacciarelli D, Pesaro E (1997) Scheduling of flexible flow lines in an automobile assembly plant. Eur J Oper Res 97:348–362. https://doi.org/10.1016/S0377-2217(96)00203-2
Wolniak R (2019) Downtime in the automotive industry production process–cause analysis. Qual Innov Prosper 23:101–118. https://doi.org/10.12776/qip.v23i2.1259
Ferreira LP, Gómez EA, Lourido GCP, Quintas JD, Tjahjono B (2012) Analysis and optimisation of a network of closed-loop automobile assembly line using simulation. Int J Adv Manuf Technol 59:351–366. https://doi.org/10.1007/s00170-011-3502-4
Kampa A, Gołda G, Paprocka I (2017) Discrete event simulation method as a tool for improvement of manufacturing systems. Computers 6:10. https://doi.org/10.3390/computers6010010
Sarda A, Digalwar AK (2018) Performance analysis of vehicle assembly line using discrete event simulation modelling. Int J Bus Excell 14:240–255. https://doi.org/10.1504/IJBEX.2018.089150
Zhuang C, Miao T, Liu J, Xiong H (2021) The connotation of digital twin, and the construction and application method of shop-floor digital twin. Robot Comput Integr Manuf 68:102075. https://doi.org/10.1016/j.rcim.2020.102075
Uhlemann THJ, Lehmann C, Steinhilper R (2017) The digital twin: realizing the cyber-physical production system for Industry 4.0. Procedia CIRP 61:335–340. https://doi.org/10.1016/j.procir.2016.11.152
Park KT, Nam YW, Lee HS, Im SJ, Noh SD, Son JY, Kim H (2019) Design and implementation of a digital twin application for a connected micro smart factory. Int J Comp Integrated Manuf 32(6):596–614. https://doi.org/10.1080/0951192x.2019.1599439
Cheng Y, Zhang Y, Ji P, Xu W, Zhou Z, Tao F (2018) Cyber-physical integration for moving digital factories forward towards smart manufacturing: a survey. Int J Adv Manuf Technol 97(1-4):1209–1221. https://doi.org/10.1007/s00170-018-2001-2
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
Park KT, Son YH, Noh SD (2020) The architectural framework of a cyber physical logistics system for digital-twin-based supply chain control. Int J Prod Res 1-22. https://doi.org/10.1080/00207543.2020.1788738
Rajkumar R, Lee I, Sha L, Stankovic J (2010) Cyber-physical systems: the next computing revolution. IEEE Design Automation Conference. https://doi.org/10.1145/1837274.1837461
Ribeiro L, Björkman M (2017) Transitioning from standard automation solutions to cyber-physical production systems: an assessment of critical conceptual and technical challenges. IEEE Syst J 12(4):3816–3827. https://doi.org/10.1109/jsyst.2017.2771139
Tao F, Cheng J, Qi Q, Zhang M, Zhang H, Sui F (2018) Digital twin-driven product design, manufacturing and service with big data. Int J Adv Manuf Technol 94(9-12):3563–3576. https://doi.org/10.1007/s00170-017-0233-1
Van Kranenburg R (2008) The Internet of Things: a critique of ambient technology and the all-seeing network of RFID. Inst Network Cultures.
Da Xu L, He W, Li S (2014) Internet of Things in industries: a survey. IEEE Trans Ind Inform 10(4):2233–2243. https://doi.org/10.1109/TII.2014.2300753
Hossain MS, Muhammad G (2016) Cloud-assisted industrial Internet of Things (IIoT)–enabled framework for health monitoring. Comp Networks 101:192–202. https://doi.org/10.1016/j.comnet.2016.01.009
Tao F, Cheng J, Qi Q (2017) IIHub: an industrial Internet-of-Things hub toward smart manufacturing based on cyber-physical system. IEEE Trans Ind Inf 14(5):2271–2280. https://doi.org/10.1109/tii.2017.2759178
Lee J, Davari H, Singh J, Pandhare V (2018) Industrial artificial intelligence for Industry 4.0-based manufacturing systems. Manuf Lett 18:20–23. https://doi.org/10.1016/j.mfglet.2018.09.002
Ren L, Zhang L, Wang L, Tao F, Chai X (2017) Cloud manufacturing: key characteristics and applications. Int J Comp Integrated Manuf 30(6):501–515. https://doi.org/10.1080/0951192x.2014.902105
Sklyar V (2017) Vedic mathematics as fast algorithms in green computing for Internet of Things. Green IT Eng: components, networks and system implementation, Springer, Cham, pp 3-22. https://doi.org/10.1007/978-3-319-55595-9_1
Atzori L, Iera A, Morabito G (2010) The Internet of Things: a survey. Comp Networks 54(15):2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010
Zezulka F, Marcon P, Vesely I, Sajdl O (2016) Industry 4.0–an introduction in the phenomenon. IFAC-PapersOnLine 49(25):8–12. https://doi.org/10.1016/j.ifacol.2016.12.002
Uhlemann TH, Schock C, Lehmann C, Freiberger S, Steinhilper R (2017) The digital twin: demonstrating the potential of real time data acquisition in production systems. Procedia Manuf 9:113–120. https://doi.org/10.1016/j.promfg.2017.04.043
Park KT, Im SJ, Kang YS, Noh SD, Kang YT, Yang SG (2019) Service-oriented platform for smart operation of dyeing and finishing industry. Int J Comp Integrated Manuf 32(3):307–326. https://doi.org/10.1080/0951192x.2019.1572225
Suyatinov SI (2020) Conceptual approach to building a digital twin of the production system. Cyber-Physical Systems: Advances in Design & Modelling. Springer, Cham. 279-290.
Ribeiro L (2017) Cyber-physical production systems' design challenges. 2017 IEEE 26th Int Symp Ind Electron (ISIE) 1189-1194. https://doi.org/10.1109/isie.2017.8001414
Cimino C, Negri E, Fumagalli L (2019) Review of digital twin applications in manufacturing. Comput Ind 113:103130. https://doi.org/10.1016/j.compind.2019.103130
Komoda N (2006) Service oriented architecture (SOA) in industrial systems. 2006 4th IEEE Int Conf Ind Inf 1-5. https://doi.org/10.1109/indin.2006.275708
Balázs D, Korondi P, Sziebig G, Thomessen T (2014) Evaluation of flexible graphical user interface for intuitive human robot interactions. Acta Polytech Hung 11:135–151. https://doi.org/10.12700/aph.11.01.2014.01.9
Park KT, Lee J, Kim HJ, Noh SD (2020) Digital twin-based cyber physical production system architectural framework for personalized production. Int J Adv Manuf Technol 106:1787–1810. https://doi.org/10.1007/s00170-019-04653-7
Lojka T, Bundzel M, Zolotová I (2016) Service-oriented architecture and cloud manufacturing. Acta Polytech Hung 13:25–44. https://doi.org/10.12700/aph.13.6.2016.6.2
Qi Q, Tao F, Hu T, Anwer N, Liu A, Wei Y, Wang L, Nee AYC (2019) Enabling technologies and tools for digital twin. J Manuf Syst 58:3–21. https://doi.org/10.1016/j.jmsy.2019.10.001
MacKenzie CM, Laskey K, McCabe F, Brown PF, Metz R, Hamilton BA (2006) Reference model for service oriented architecture 1.0. OASIS standard. 12(S 18). http://docs.oasis-open.org/soa-rm/v1.0/
Papazoglou MP, Traverso P, Dustdar S, Leymann F (2007) Service-oriented computing: state of the art and research challenges. Computer 40(11):38–45. https://doi.org/10.1109/mc.2007.400
Zhang Y, Zhang G, Wang J, Sun S, Si S, Yang T (2015) Real-time information capturing and integration framework of the Internet of manufacturing things. Int J Comp Integrated Manufact 28(8):811–822. https://doi.org/10.1080/0951192x.2014.900874
Zhu T, Dhelim S, Zhou Z, Yang S, Ning H (2017) An architecture for aggregating information from distributed data nodes for industrial Internet of Things. Comput Electr Eng 58:337–349. https://doi.org/10.1016/j.compeleceng.2016.08.018
Jerstad I, Dustdar S, Thanh DV (2005) A service oriented architecture framework for collaborative services. 14th IEEE Int Workshops Enabling Technol: Infrastructure for Collaborative Enterprise (WETICE'05) 121-125. https://doi.org/10.1109/wetice.2005.11
Papazoglou MP (2003) Service-oriented computing: concepts, characteristics and directions. Proc Fourth Int Conf Web Info Sys Eng (WISE) 3-12. https://doi.org/10.1109/WISE.2003.1254461
https://media.jaguarlandrover.com/en-gb/2016/manufacturing-success-story (Accessed 18 November 2020)
Park KT, Yang J, Noh SD (2020) VREDI: virtual representation for a digital twin application in a work-center-level asset administration shell. J Intell Manuf 32:1–44. https://doi.org/10.1007/s10845-020-01586-x
http://www.shym.co.kr/en/02-02/ (Accessed 18 November 2020)
Park KT, Lee D, Do Noh S (2020) Operation procedures of a work-center-level digital twin for sustainable and smart manufacturing. Int J Prec Eng & Manuf-Green Technol 7:791–814
Wiyono DS, Rudy N BP (2013) Development of enterprise resource planning applications to help resources management of furniture company using technology windows communication foundation. J@ti Undip: Jurnal Teknik Industri 8:153-160. https://doi.org/10.12777/jati.8.3.153-160
Liu M (2008) WCF multi-tier services development with LINQ. Packt Publishing Ltd.
Funding
This work was supported by the KEIT (20003957, Simulation and Optimization of Logistics Operation of Big Data Base Manufacturing Line) and the WC300 Project (S2482274, Development of Multi-vehicle Flexible Manufacturing Platform Technology for Future Smart Automotive Body Production) funded by the Ministry of SMEs and Startups.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Son, Y.H., Park, K.T., Lee, D. et al. Digital twin–based cyber-physical system for automotive body production lines. Int J Adv Manuf Technol 115, 291–310 (2021). https://doi.org/10.1007/s00170-021-07183-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-021-07183-3