Top

Journal of Intelligent Manufacturing

Published in:

27-09-2022

A digital shadow framework using distributed system concepts

Authors: Ayman AboElHassan, Soumaya Yacout

Published in: Journal of Intelligent Manufacturing | Issue 8/2023

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Digital twin (DT) is a research topic that gained momentum in the Industry 4.0 era. The goal of DT is to create a virtual real-time intelligent system that is a typical twin of the physical system. DT provides analysis, prognosis, planning, and rapid response when needed. Digital shadow (DS) is an artifact concept of DT that provides a real-time replica of the physical system. Information in a DS is passed in one direction only, from the physical system to the virtual one. While in DT, the information goes in both directions. The definition and roles of DS and DT are overlapping. However, DS can be defined as the main component of a DT system. In this paper, DS roles are specified. Based on these roles, A DS framework architecture is proposed, and the communication system between its components. The proposed framework design is built using distributed system concepts such as event-driven architecture, microservices, and containerization. These concepts are well defined and utilized in the software engineering domain. The originality of the proposed framework is the definition of a systematic approach for designing and integrating digital models (DMs) from different vendors and domains. An experiment is designed to prove the framework’s ability to shadow a physical system in real-time. Multiple DMs are implemented and deployed on the proposed DS framework. These DMs are used to shadow a natural gas compressor system. Experimental results prove the practicality of our proposed DS framework to operate in real-time.

previous article Conductive particle detection via efficient encoder–decoder network

next article CNN and ensemble learning based wafer map failure pattern recognition based on local property based features

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

AboElHassan, A., Sakr, A., & Yacout, S. (2021). A framework for digital twin deployment in production systems. In P. Weißgraeber, F. Heieck, & C. Ackermann (Eds.), Advances in automotive production technology-theory and application (pp. 145–152). Springer.

Alaasam, A.B.A., Radchenko, G., & Tchernykh, A. (2019). Stateful stream processing for digital twins: Microservice-based kafka stream DSL. In 2019 international multi-conference on engineering, computer and information sciences (SIBIRCON) (pp. 0804–0809). https://doi.org/10.1109/SIBIRCON48586.2019.8958367

Alexopoulos, K., Nikolakis, N., & Chryssolouris, G. (2020). Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing. International Journal of Computer Integrated Manufacturing, 33(5), 429–439. https://doi.org/10.1080/0951192X.2020.1747642CrossRef

Becker, F., Bibow, P., Dalibor, M., Gannouni, A., Hahn, V., Hopmann, C., Jarke, M., Koren, I., Kröger, M., Lipp, J., Maibaum, J., Michael, J., Rumpe, B., Sapel, P., Schäfer, N., Schmitz, G., Schuh, G., & Wortmann, A. (2021). A conceptual model for digital shadows in industry and its application. Lecture notes in computer science (LNCS). In A. Ghose, J. Horkoff, V. E. Silva Souza, et al. (Eds.), Conceptual modeling (Vol. 13011, pp. 271–281). Springer.

Borghesi, A., Di Modica, G., Bellavista, P., Gowtham, V., Willner, A., Nehls, D., Kintzler, F., Cejka, S., Tisbeni, S. R., Costantini, A., Galletti, M., Antonacci, M., & Ahouangonou, J. C. (2021). Iotwins: Design and implementation of a platform for the management of digital twins in industrial scenarios. In 2021 IEEE/ACM 21st international symposium on cluster, cloud and internet computing (CCGrid) (pp 625–633). https://doi.org/10.1109/CCGrid51090.2021.00075

Brauner, P., Dalibor, M., Jarke, M., Kunze, I., Koren, I., Lakemeyer, G., et al. (2022). A computer science perspective on digital transformation in production. ACM Transactions on Internet of Things, 3(2), 1–32. https://doi.org/10.1145/3502265.CrossRef

Brecher, C., Buchsbaum, M., Müller, A., Schilling, K., Obdenbusch, M., Staudacher, S., & Albasatineh, M. C. (2021). Gaining IIOT insights by leveraging ontology-based modelling of raw data and digital shadows. In 2021 4th IEEE international conference on industrial cyber-physical systems (ICPS) (pp 231–236). https://doi.org/10.1109/ICPS49255.2021.9468116

Catarci, T., Firmani, D., Leotta, F., Mandreoli, F., Mecella, M., & Sapio, F. (2019). A conceptual architecture and model for smart manufacturing relying on service-based digital twins. In 2019 IEEE international conference on web services (ICWS). IEEE. https://doi.org/10.1109/icws.2019.00047

Cimino, C., Negri, E., & Fumagalli, L. (2019). Review of digital twin applications in manufacturing. Computers in Industry, 113(103), 130. https://doi.org/10.1016/j.compind.2019.103130CrossRef

Cognite. (2018). Open industrial data: Inspiring innovation & fueling collaboration in the oil & gas ecosystem. Tech. rep., Cognite. https://content.cognite.com/open-industrial-data

de Castro-Cros, M., Velasco, M., & Angulo, C. (2021). Machine-learning-based condition assessment of gas turbines—A review. Energies, 14, 8468. https://doi.org/10.3390/en14248468CrossRef

Dobaj, J., Krisper, M., Macher, G. (2019). Towards cyber-physical infrastructure as-a-service (CPIaaS) in the era of industry 4.0. In Communications in computer and information science (pp. 310–321). Springer. https://doi.org/10.1007/978-3-030-28005-5_24

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.CrossRef

Göppert, A., Grahn, L., Rachner, J., Grunert, D., Hort, S., & Schmitt, R. H. (2021). Pipeline for ontology-based modeling and automated deployment of digital twins for planning and control of manufacturing systems. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01860-6.CrossRef

Groshev, M., Guimarães, C., Martén-Pérez, J., & de la Oliva, A. (2021). Toward intelligent cyber-physical systems: Digital twin meets artificial intelligence. IEEE Communications Magazine, 59(8), 14–20. https://doi.org/10.1109/MCOM.001.2001237.CrossRef

Hu, F., Qiu, X., Jing, G., Tang, J., & Zhu, Y. (2022). Digital twin-based decision making paradigm of raise boring method. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-022-01941-0.CrossRef

Huang, Z., Shen, Y., Li, J., Fey, M., & Brecher, C. (2021). A survey on AI-driven digital twins in industry 4.0: Smart manufacturing and advanced robotics. Sensors. https://doi.org/10.3390/s21196340

Jiang, Z., Guo, Y., & Wang, Z. (2021). Digital twin to improve the virtual-real integration of industrial IoT. Journal of Industrial Information Integration, 22(100), 196. https://doi.org/10.1016/j.jii.2020.100196CrossRef

Kamath, V., Morgan, J., & Ali, M.I. (2020). Industrial IoT and digital twins for a smart factory : An open source toolkit for application design and benchmarking. In 2020 global internet of things summit (GIoTS) (pp. 1–6). https://doi.org/10.1109/GIOTS49054.2020.9119497

Kareem, L. A., Iwalewa, T. M., & Al-Marhoun, M. (2016). New explicit correlation for the compressibility factor of natural gas: Linearized z-factor isotherms. Journal of Petroleum Exploration and Production Technology, 6(3), 481–492. https://doi.org/10.1007/s13202-015-0209-3CrossRef

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. https://doi.org/10.3390/pr9081362.CrossRef

Konstantinov, S., Assad, F., Azam, W., Vera, D., Ahmad, B., & Harrison, R. (2021). Developing web-based digital twin of assembly lines for industrial cyber-physical systems. In 2021 4th IEEE international conference on industrial cyber-physical systems (ICPS) (pp. 219–224). https://doi.org/10.1109/ICPS49255.2021.9468227

Kreps, J., Narkhede, N., & Rao, J. (2011). Kafka: A distributed messaging system for log processing. In Proceedings of the NetDB (pp. 1–7)

Kuehner, K. J., Scheer, R., & Strassburger, S. (2021). Digital twin: Finding common ground—A meta-review. Procedia CIRP, 104, 1227–1232. https://doi.org/10.1016/j.procir.2021.11.206CrossRef

Kunath, M., & Winkler, H. (2018). Integrating the digital twin of the manufacturing system into a decision support system for improving the order management process. Procedia CIRP, 72, 225–231. https://doi.org/10.1016/j.procir.2018.03.192CrossRef

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.CrossRef

Leng, B., Sun, H., Si, G., Xia, T., & Wang, H. (2021). Digital twin and manufacturing simulation integrated platform embedded in cyber-physical system. Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/1983/1/012117

Liu, S., Wang, X. V., & Wang, L. (2022). Digital twin-enabled advance execution for human-robot collaborative assembly. CIRP Annals, 71(1), 25–28. https://doi.org/10.1016/j.cirp.2022.03.024CrossRef

López, C. E. B. (2021). Real-time event-based platform for the development of digital twin applications. The International Journal of Advanced Manufacturing Technology, 116(3), 835–845. https://doi.org/10.1007/s00170-021-07490-9CrossRef

Lu, Y., Liu, C., Wang, K. I. K., Huang, H., & Xu, X. (2020). Digital twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics and Computer-Integrated Manufacturing, 61(101), 837. https://doi.org/10.1016/j.rcim.2019.101837.CrossRef

Lüdtke, K. H. (2004). Process centrifugal compressors (1st ed.). Springer.

Martinez, S., Mariño, A., Sanchez, S., Montes, A. M., Triana, J. M., Barbieri, G., et al. (2021). A digital twin demonstrator to enable flexible manufacturing with robotics: A process supervision case study. Production & Manufacturing Research, 9(1), 140–156. https://doi.org/10.1080/21693277.2021.1964405.CrossRef

Mykoniatis, K., & Harris, G. A. (2021). A digital twin emulator of a modular production system using a data-driven hybrid modeling and simulation approach. Journal of Intelligent Manufacturing, 32(7), 1899–1911. https://doi.org/10.1007/s10845-020-01724-5CrossRef

Paredis, R., & Vangheluwe, H. (2021). Exploring a digital shadow design workflow by means of a line following robot use-case. In 2021 Annual modeling and simulation conference (ANNSIM) (pp. 1–12). https://doi.org/10.23919/ANNSIM52504.2021.9552143

Park, H., Easwaran, A., & Andalam, S. (2019). TiLA: Twin-in-the-loop architecture for cyber-physical production systems. In 2019 IEEE 37th international conference on computer design (ICCD). IEEE. https://doi.org/10.1109/iccd46524.2019.00019

Park, K. T., Lee, S. H., & Noh, S. D. (2021). Information fusion and systematic logic library-generation methods for self-configuration of autonomous digital twin. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01795-yCrossRef

Pires, F., Melo, V., Almeida, J., & Leitão, P. (2020). Digital twin experiments focusing virtualisation, connectivity and real-time monitoring. In 2020 IEEE conference on industrial cyberphysical systems (ICPS) (pp. 309–314). https://doi.org/10.1109/ICPS48405.2020.9274739

Qamsane, Y., Chen, C. Y., Balta, E. C., Kao, B. C., Mohan, S., Moyne, J., Tilbury, D., & Barton, K. (2019). A unified digital twin framework for real-time monitoring and evaluation of smart manufacturing systems. In 2019 IEEE 15th international conference on automation science and engineering (CASE). IEEE. https://doi.org/10.1109/coase.2019.8843269

Qi, Q., Tao, F., Hu, T., Answer, N., Liu, A., Wei, Y., Wang, L., & Nee, A. Y. C. (2021). Enabling technologies and tools for digital twin. Journal of Manufacturing Systems, 58, 3–21. https://doi.org/10.1016/j.jmsy.2019.10.001.

Radchenko, G., Alaasam, A.B., & Tchernykh, A. (2018). Micro-workflows: Kafka and kepler fusion to support digital twins of industrial processes. In 2018 IEEE/ACM international conference on utility and cloud computing companion (UCC Companion) (pp. 83–88). https://doi.org/10.1109/UCC-Companion.2018.00039

Redeker, M., Weskamp, J. N., Rössl, B., & Pethig, F. (2021). Towards a digital twin platform for industrie 4.0. In 2021 4th IEEE international conference on industrial cyber-physical systems (ICPS) (pp. 39–46). https://doi.org/10.1109/ICPS49255.2021.9468204

Redelinghuys, A. J. H., Basson, A. H., & Kruger, K. (2020). A six-layer architecture for the digital twin: A manufacturing case study implementation. Journal of Intelligent Manufacturing, 31(6), 1383–1402. https://doi.org/10.1007/s10845-019-01516-6CrossRef

Riesener, M., Schuh, G., Dölle, C., & Tönnes, C. (2019). The digital shadow as enabler for data analytics in product life cycle management. Procedia CIRP, 80, 729–734. https://doi.org/10.1016/j.procir.2019.01.083.CrossRef

Sax, M. J. (2018). Apache Kafka (pp. 1–8). Springer.

Schuh, G., Kelzenberg, C., Wiese, J., & Ochel, T. (2019). Data structure of the digital shadow for systematic knowledge management systems in single and small batch production. Procedia CIRP, 84, 1094–1100. https://doi.org/10.1016/j.procir.2019.04.210.CrossRef

Sepasgozar, S. M. E. (2021). Differentiating digital twin from digital shadow: Elucidating a paradigm shift to expedite a smart, sustainable built environment. Buildings. https://doi.org/10.3390/buildings11040151CrossRef

Shafto, M., Conroy, M., Doyle, R., et al. (2010). Draft modeling, simulation, information technology & processing roadmap. Technology Area 11

Stan, L., Nicolescu, A. F., Pupăză, C., & Jiga, G. (2022). Digital twin and web services for robotic deburring in intelligent manufacturing. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-022-01928-x.CrossRef

Stark, R., Damerau, T. (2019). Digital twin (pp 1–8). Springer. https://doi.org/10.1007/978-3-642-35950-7_16870-1

Steindl, G., & Kastner, W. (2021). Semantic microservice framework for digital twins. Applied Sciences. https://doi.org/10.3390/app11125633CrossRef

Talkhestani, B. A., & Weyrich, M. (2020). Digital twin of manufacturing systems: A case study on increasing the efficiency of reconfiguration. Automatisierungstechnik, 68(6), 435–444. https://doi.org/10.1515/auto-2020-0003CrossRef

Tancredi, G. P., Vignali, G., & Bottani, E. (2022). Integration of digital twin, machine-learning and industry 4.0 tools for anomaly detection: An application to a food plant. Sensors. https://doi.org/10.3390/s22114143CrossRef

Tao, F., Qi, Q., Wang, L., & Nee, A. Y. C. (2019). 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., & 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.2017.2756069CrossRef

Tliba, K., Diallo, T. M. L., Penas, O., Ben Khalifa, R., Ben Yahia, N., & Choley, J. Y. (2022). Digital twin-driven dynamic scheduling of a hybrid flow shop. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-022-01922-3.CrossRef

Tong, X., Liu, Q., Pi, S., & Xiao, Y. (2020). Real-time machining data application and service based on IMT digital twin. Journal of Intelligent Manufacturing, 31(5), 1113–1132. https://doi.org/10.1007/s10845-019-01500-0.CrossRef

Trauer, J., Schweigert-Recksiek, S., Engel, C., Spreitzer, K., & Zimmermann, M. (2020). What is a digital twin?–Definitions and insights from an industrial case study in technical product development. Proceedings of the Design Society: DESIGN Conference, 1, 757–766. https://doi.org/10.1017/dsd.2020.15.CrossRef

Uhlenkamp, J. F., Hribernik, K., Wellsandt, S., & Thoben, K. D. (2019). Digital twin applications : A first systemization of their dimensions. In. (2019). IEEE international conference on engineering, technology and innovation (ICE/ITMC). IEEE. https://doi.org/10.1109/ice.2019.8792579.CrossRef

Vayghan, L. A., Saied, M. A., Toeroe, M., & Khendek, F. (2021). A Kubernetes controller for managing the availability of elastic microservice based stateful applications. Journal of Systems and Software, 175(110), 924. https://doi.org/10.1016/j.jss.2021.110924.CrossRef

Weber, C., Königsberger, J., Kassner, L., & Mitschang, B. (2017). M2ddm—A maturity model for data-driven manufacturing. Procedia CIRP, 63, 173–178. https://doi.org/10.1016/j.procir.2017.03.309

Williams, R., Erkoyuncu, J. A., Masood, T., & Vrabic, R. (2020). Augmented reality assisted calibration of digital twins of mobile robots. IFAC-PapersOnLine, 53(3), 203–208. https://doi.org/10.1016/j.ifacol.2020.11.033

Yun, S., Park, J.H., & Kim, W.T. (2017). Data-centric middleware based digital twin platform for dependable cyber-physical systems. In 2017 ninth international conference on ubiquitous and future networks (ICUFN). IEEE. https://doi.org/10.1109/icufn.2017.7993933

Zhang, H., Qi, Q., & Tao, F. (2022). A multi-scale modeling method for digital twin shop-floor. Journal of Manufacturing Systems, 62, 417–428. https://doi.org/10.1016/j.jmsy.2021.12.011CrossRef

Zhong, Z., & Buyya, R. (2020). A cost-efficient container orchestration strategy in Kubernetes-based cloud computing infrastructures with heterogeneous resources. ACM Transactions on Internet Technology. https://doi.org/10.1145/3378447CrossRef

Zhu, Z., Xi, X., Xu, X., & Cai, Y. (2021). Digital twin-driven machining process for thin-walled part manufacturing. Journal of Manufacturing Systems, 59, 453–466. https://doi.org/10.1016/j.jmsy.2021.03.015

Zhuang, C., Gong, J., & Liu, J. (2021). Digital twin-based assembly data management and process traceability for complex products. Journal of Manufacturing Systems, 58, 118–131. https://doi.org/10.1016/j.jmsy.2020.05.011CrossRef

Zhuang, C., Liu, J., & Xiong, H. (2018). Digital twin-based smart production management and control framework for the complex product assembly shop-floor. The International Journal of Advanced Manufacturing Technology, 96(1–4), 1149–1163. https://doi.org/10.1007/s00170-018-1617-6CrossRef

Title: A digital shadow framework using distributed system concepts
Authors: Ayman AboElHassan
Soumaya Yacout
Publication date: 27-09-2022
Publisher: Springer US
Published in: Journal of Intelligent Manufacturing / Issue 8/2023
Print ISSN: 0956-5515
Electronic ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-022-02028-6

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Other articles of this Issue 8/2023

Manufacturing process classification based on heat kernel signature and convolutional neural networks

Integrated scheduling of production, inventory and imperfect maintenance based on mutual feedback of supplier and demander in distributed environment

A universal predictor-based machine learning model for optimal process maps in laser powder bed fusion process

Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clustering

A novel feature-fusion-based end-to-end approach for remaining useful life prediction

Meta-FSDet: a meta-learning based detector for few-shot defects of photovoltaic modules

Premium Partners