DES and IIoT fusion approach towards real-time synchronization of physical and digital components in manufacturing processes
DOI:
https://doi.org/10.31181/rme040115092023mKeywords:
Digital Twin, IMU, Discrete Event Simulation, Smart ManufacturingAbstract
Today's manufacturing systems offer more products to meet specific needs. Complex production systems in rapidly changing environments result from product variation, shorter product life cycles, and supply chain expansion. A cyber-physical production system (CPPS) can use manufacturing and logistics data to plan, monitor, and control production. Discrete event simulation (DES) and digital twin (DT) technology can model and evaluate manufacturing and logistics processes using high-level decision support and process monitoring. The cost of collecting input data from different enterprise data sources and mapping it into models and the lack of qualified experts prevent the widespread use of these methods in industry, especially in small and medium-sized enterprises and larger multinational companies. This research aims to create a modular digital twin framework for manufacturing process optimization and real-time monitoring in an industrial environment with few components. The system can identify and track the product through the manufacturing cycle while updating the DT in real-time and can be used independently to collect input parameters for discrete event-driven simulations and even for automatic simulation building in the future. The framework's operation will be shown through an example. With the proposed IIoT (industrial internet of things) system integration, it can detect faults and warn of deviations from normal operation, and DT can drastically reduce data collection and model building and support model reusability, increasing sustainability.
References
Agalianos, K., Ponis, S. T., Aretoulaki, E., Plakas, G., & Efthymiou, O. (2020). Discrete Event Simulation and Digital Twins: Review and Challenges for Logistics. Procedia Manufacturing, 51, 1636–1641. https://doi.org/10.1016/j.promfg.2020.10.228.
Barlas, P., & Heavey, C. (2016). Automation of input data to discrete event simulation for manufacturing: A review. International Journal of Modeling, Simulation, and Scientific Computing, 07(01), Article 01. https://doi.org/10.1142/S1793962316300016.
Boyes, H., & Watson, T. (2022). Digital twins: An analysis framework and open issues. Computers in Industry, 143, 103763. https://doi.org/10.1016/j.compind.2022.103763.
Dafflon, B., Moalla, N., & Ouzrout, Y. (2021). The challenges, approaches, and used techniques of CPS for manufacturing in Industry 4.0: A literature review. The International Journal of Advanced Manufacturing Technology, 113(7–8), 2395–2412. https://doi.org/10.1007/s00170-020-06572-4.
Delgado Sobrino, D. R., Rychtarik, V., & Cagáňová, D. (2022). Digital Twin design at the material flow level. Journal of Physics: Conference Series, 2212(1), 012012. https://doi.org/10.1088/1742-6596/2212/1/012012.
Ding, K., Chan, F. T. S., Zhang, X., Zhou, G., & Zhang, F. (2019). Defining a Digital Twin-based Cyber-Physical Production System for autonomous manufacturing in smart shop floors. International Journal of Production Research, 57(20), 6315–6334. https://doi.org/10.1080/00207543.2019.1566661.
Feng, H., Gomes, C., & Larsen, P. G. (2023). Model-Based Monitoring and State Estimation for Digital Twins: The Kalman Filter (arXiv:2305.00252). arXiv. http://arxiv.org/abs/2305.00252.
Garrido, J., & Sáez, J. (2019). Integration of automatic generated simulation models, machine control projects and management tools to support whole life cycle of industrial digital twins. IFAC-PapersOnLine, 52(13), 1814–1819. https://doi.org/10.1016/j.ifacol.2019.11.465.
Haraszkó, C., & Németh, I. (2015). DES Configurators for Rapid Virtual Prototyping and Optimisation of Manufacturing Systems. Periodica Polytechnica Mechanical Engineering, 59(3), 143–152. https://doi.org/10.3311/PPme.7888.
Hughes, K., Fernando, H., Szkilnyk, G., Surgenor, B., & Greenspan, M. (2014). Video event detection for fault monitoring in assembly automation. International Journal of Intelligent Systems Technologies and Applications, 13(1/2), 103. https://doi.org/10.1504/IJISTA.2014.059302.
Kassen, S., Tammen, H., Zarte, M., & Pechmann, A. (2021). Concept and Case Study for a Generic Simulation as a Digital Shadow to Be Used for Production Optimisation. Processes, 9(8), 1362. https://doi.org/10.3390/pr9081362.
Kepper, J. H., Claus, B. C., & Kinsey, J. C. (2019). A Navigation Solution Using a MEMS IMU, Model-Based Dead-Reckoning, and One-Way-Travel-Time Acoustic Range Measurements for Autonomous Underwater Vehicles. IEEE Journal of Oceanic Engineering, 44(3), 664–682. https://doi.org/10.1109/JOE.2018.2832878.
Kocsis Szürke, S., Kovács, G., Sysyn, M., Liu, J., & Fischer, S. (2023). Numerical Optimization of Battery Heat Management of Electric Vehicles. Journal of Applied and Computational Mechanics, 9(4), 1076–1092. doi: https://doi.org/10.22055/jacm.2023.43703.4119.
Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (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.
Ladj, A., Wang, Z., Meski, O., Belkadi, F., Ritou, M., & Da Cunha, C. (2021). A knowledge-based Digital Shadow for machining industry in a Digital Twin perspective. Journal of Manufacturing Systems, 58, 168–179. https://doi.org/10.1016/j.jmsy.2020.07.018.
Lee, C. G., & Park, S. C. (2014). Survey on the virtual commissioning of manufacturing systems. Journal of Computational Design and Engineering, 1(3), 213–222. https://doi.org/10.7315/JCDE.2014.021.
Monek, G. D., & Fischer, S. (2023). IIoT-Supported Manufacturing-Material-Flow Tracking in a DES-Based Digital-Twin Environment. Infrastructures, 8(4), 75. https://doi.org/10.3390/infrastructures8040075.
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 Annals, 65(2), 621–641. https://doi.org/10.1016/j.cirp.2016.06.005.
Németh, A., Fischer, S. (2021). Investigation of the Glued Insulated Rail Joints Applied to CWR Tracks. Facta Universitatis. Series: Mechanical Engineering, 19(4), 681–704. https://doi.org/10.22190/FUME210331040N.
Park, H.-T., Kwak, J.-G., Wang, G.-N., & Park, S. C. (2010). Plant model generation for PLC simulation. International Journal of Production Research, 48(5), 1517–1529. https://doi.org/10.1080/00207540802577961.
Pfeiffer, A., Kádár, B., Popovics, G., Kardos, C., Vén, Z., Kemény, L., & Monostori, L. (2012). Applying model-reconstruction by exploring MES and PLC data for simulation support of production systems. In Proceedings of the 2012 Winter Simulation Conference (WSC), Berlin, Germany, 1–13, https://doi.org/10.1109/WSC.2012.6465069.
Popovics, G., & Monostori, L. (2013). ISA Standard Simulation Model Generation Supported by Data Stored in Low Level Controllers. Procedia CIRP, 12, 432–437. https://doi.org/10.1016/j.procir.2013.09.074.
Prat, S., Cavron, J., Kesraoui, D., Rauffet, P., Berruet, P., & Bignon, A. (2017). An Automated Generation Approach of Simulation Models for Checking Control/Monitoring System. IFAC-PapersOnLine, 50(1), 6202–6207. https://doi.org/10.1016/j.ifacol.2017.08.1014.
Reinhardt, H., Weber, M., & Putz, M. (2019). A Survey on Automatic Model Generation for Material Flow Simulation in Discrete Manufacturing. Procedia CIRP, 81, 121–126. https://doi.org/10.1016/j.procir.2019.03.022.
Ruppert, T., & Abonyi, J. (2018). Software Sensor for Activity-Time Monitoring and Fault Detection in Production Lines. Sensors, 18(7), 2346. https://doi.org/10.3390/s18072346.
Szalai, S., Kocsis Szürke, S., Harangozó, D., Fischer, S. (2022). Investigation of deformations of a lithium polymer cell using the Digital Image Correlation Method (DICM). Reports in Mechanical Engineering, 3(1), 116–134. https://doi.org/10.31181/rme20008022022s.
Szántó, N., Csapó, Á., & Horváth, I. (2023). Information Basis of Digital Twins: A Quantifiable Metric for Spatio-Temporal Expressivity. Acta Polytechnica Hungarica, 20(6), 151–171. https://doi.org/10.12700/APH.20.6.2023.6.9.
Tao, F., & Zhang, M. (2017). Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing. IEEE Access, 5, 20418–20427. https://doi.org/10.1109/ACCESS.2017.2756069.
Turner, C. J., & Garn, W. (2022). Next generation DES simulation: A research agenda for human centric manufacturing systems. Journal of Industrial Information Integration, 28, 100354. https://doi.org/10.1016/j.jii.2022.100354.
Vieira, A. A. C., Dias, L. M. S., Santos, M. Y., Pereira, G. A. B., & Oliveira, J. A. (2018). Setting an Industry 4.0 Research and Development Agenda for Simulation – a Literature Review. International Journal of Simulation Modelling, 17(3), 377–390. https://doi.org/10.2507/IJSIMM17(3)429.
Zdravković, M., & Korunović, N. (2023). Novel methodology for real-time structural analysis assistance in custom product design. Facta Universitatis, Series: Mechanical Engineering, 21(2), 293-305. https://doi.org/10.22190/FUME200828008Z.
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