Abstract
The adoption of a digital twin for a smart factory offers several advantages, such as improved production and reduced costs, and energy consumption. Due to the growing demands of the market, factories have adopted the reconfigurable manufacturing paradigm, wherein the structure of the factory is constantly changing. This situation presents a unique challenge to traditional modeling and simulation approaches. To deal with this scenario, we propose a generic data-driven framework for automated construction of digital twins for smart factories. The novel aspects of our proposed framework include a pure data-driven approach incorporating machine learning and process mining techniques, and continuous model improvement and validation.
Keywords
- Data-driven
- Digital twin
- Simulation
- Smart factory
- Reconfigurable manufacturing
- Machine learning
- Process mining
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Grieves, M.: Digital twin: manufacturing excellence through virtual factory replication. White Pap. 1, 1–7 (2014)
Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W.: Digital twin in manufacturing: a categorical literature review and classification. IFAC-PapersOnLine 51(11), 1016–1022 (2018)
Tao, F., Zhang, H., Liu, A., Nee, A.Y.: Digital twin in industry: state-of-the-art. IEEE Trans. Industr. Inf. 15(4), 2405–2415 (2018)
Söderberg, R., Wärmefjord, K., Carlson, J.S., Lindkvist, L.: Toward a digital twin for real-time geometry assurance in individualized production. CIRP Ann. 66(1), 137–140 (2017)
Law, A.M., Kelton, W.D., Kelton, W.D.: Simulation Modeling and Analysis, vol. 3. McGraw-Hill, New York (2000)
Tao, F., Zhang, M.: Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access 5, 20418–20427 (2017)
Koren, Y., Gu, X., Guo, W.: Reconfigurable manufacturing systems: principles, design, and future trends. Front. Mech. Eng. 13(2), 121–136 (2018)
Radziwon, A., Bilberg, A., Bogers, M., Madsen, E.S.: The smart factory: exploring adaptive and flexible manufacturing solutions. Procedia Eng. 69, 1184–1190 (2014)
Gola, A., Świć, A.: Simulation based analysis of reconfigurable manufacturing system configurations. In: Applied Mechanics and Materials, vol. 844, pp. 50–59. Trans Tech Publ (2016)
Zhang, C., Xu, W., Liu, J., Liu, Z., Zhou, Z., Pham, D.T.: A reconfigurable modeling approach for digital twin-based manufacturing system. Procedia CIRP 83, 118–125 (2019)
Wang, X.V., Wang, L.: Digital twin-based WEEE recycling recovery, and remanufacturing in the background of industry 4.0. Int. J. Prod. Res. 57(12), 3892–3902 (2019)
Yang, S., MR, A.R., Kaminski, J., Pepin, H.: Opportunities for industry 4.0 to support remanufacturing. Appl. Sci. 8(7), 1177 (2018)
Kim, B.S., Kang, B.G., Choi, S.H., Kim, T.G.: Data modeling versus simulation modeling in the big data era: case study of a greenhouse control system. Simulation 93(7), 579–594 (2017)
Qi, Y., Mao, Z., Zhang, M., Guo, H.: Manufacturing practices and servitization: the role of mass customization and product innovation capabilities. Int. J. Prod. Econ. 107747 (2020)
Lattner, A.D., Bogon, T., Lorion, Y., Timm, I.J.: A knowledge-based approach to automated simulation model adaptation. In: Proceedings of the 2010 Spring Simulation Multiconference, pp. 1–8 (2010)
Charpentier, P., Véjar, A.: From spatio-temporal data to manufacturing system model. J. Control Autom. Electr. Syst. 25(5), 557–565 (2014)
Rodič, B., Kanduč, T.: Optimisation of a complex manufacturing process using discrete event simulation and a novel heuristic algorithm. Int. J. Math. Models Methods Appl. Sci. 9, 320–329 (2015)
Uhlemann, T.H.J., Lehmann, C., Steinhilper, R.: The digital twin: realizing the cyber-physical production system for industry 4.0. Procedia CIRP 61, 335–340 (2017)
Goodall, P., Sharpe, R., West, A.: A data-driven simulation to support remanufacturing operations. Comput. Ind. 105, 48–60 (2019)
Rasheed, A., San, O., Kvamsdal, T.: Digital twin: values, challenges and enablers from a modeling perspective. IEEE Access 8, 21980–22012 (2020)
Lazarova-Molnar, S., Mohamed, N.: Reliability assessment in the context of industry 4.0: data as a game changer. Procedia Comput. Sci. 151, 691–698 (2019)
Ali Farsi, M., Zio, E.: Industry 4.0: Some challenges and opportunities for reliability engineering
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 1–58 (2009)
Francis, D.P., Raimond, K.: A random fourier features based streaming algorithm for anomaly detection in large datasets. In: Rajsingh, E., Veerasamy, J., Alavi, A., Peter, J. (eds.) Advances in Big Data and Cloud Computing, vol. 645, pp. 209–217. Springer, Singapore (2018)
Van Der Aalst, W.: Process mining. Commun. ACM 55(8), 76–83 (2012)
Haystack: Haystack, project haystack. https://project-haystack.org/ (2020). Accessed 1 August 2020
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Francis, D.P., Lazarova-Molnar, S., Mohamed, N. (2021). Towards Data-Driven Digital Twins for Smart Manufacturing. In: Selvaraj, H., Chmaj, G., Zydek, D. (eds) Proceedings of the 27th International Conference on Systems Engineering, ICSEng 2020. ICSEng 2020. Lecture Notes in Networks and Systems, vol 182. Springer, Cham. https://doi.org/10.1007/978-3-030-65796-3_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-65796-3_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-65795-6
Online ISBN: 978-3-030-65796-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)