Digital Twin Driven Assembly Line Re-Balancing and Decision Support
DOI:
https://doi.org/10.26034/lu.akwi.2024.6233Keywords:
Digital twins, assembly lines, production control, Industry 5.0, work assignment, re-balancingAbstract
Recent investments in industrial digitization together with the concrete need for short-term planning capabilities mean digital twins can effectively aid enterprises in the management of their production systems and value chains. This paper introduces a conceptual framework for assembly line re-balancing in the context of Industry 5.0, focusing on manual assembly processes. The framework aims to leverage a digital twin for obtaining a synchronized representation of the current task allocations in the assembly line, and uses data-driven scenario generation methods for investigating alternative balancing solutions that are proposed to operators in real time. A proof-of-concept platform is implemented in a laboratory environment, utilizing an assembly line with industrial components. Preliminary results demonstrate the compatibility of the proposed components within the digital twin framework. The potential applicability to various manual assembly scenarios is discussed, along with considerations for incorporating additional constraints in the evaluation process.
References
Ayse Aslan, Hanane El-Raoui, Jack Hanson, Gokula Vasantha, John Quigley, and Jonathan Corney. Data-driven discovery of manufacturing processes and performance from worker localisation. In International Conference on Flexible Automation and Intelligent Manufacturing, pages 592–602. Springer, 2023. https://doi.org/10.1007/978-3-031-38241-3_67
Daria Battini, Serena Finco, and Fabio Sgarbossa. Human-oriented assembly line balancing and sequencing model in the industry 4.0 era. Scheduling in Industry 4.0 and Cloud Manufacturing, pages 141–165, 2020. https://doi.org/10.1007/978-3-030-43177-8_8
Christian Becker and Armin Scholl. A survey on problems and methods in generalized assembly line balancing. European journal of operational research, 168(3):694–715, 2006. https://doi.org/10.1016/j.ejor.2004.07.023
Georgios L Bounitsis, Lazaros G Papageorgiou, and Vassilis M Charitopoulos. Data-driven scenario generation for twostage
stochastic programming. Chemical Engineering Research and Design, 187:206–224, 2022. https://doi.org/10.1016/j.cherd.2022.08.014
Nils Boysen, Philipp Schulze, and Armin Scholl. Assembly line balancing: What happened in the last fifteen years? European Journal of Operational Research, 301(3):797–814, 2022. https://doi.org/10.1016/j.ejor.2021.11.043
Tolga Cimen, Adil Baykaso˘glu, and S Akyol. Assembly line rebalancing and worker assignment considering ergonomic risks in an automotive parts manufacturing plant. International Journal of Industrial Engineering Computations, 13 (3):363–384, 2022. https://doi.org/10.5267/j.ijiec.2022.2.001
Marcello Colledani, Anteneh Yemane, Giovanni Lugaresi, Giovanni Borzi, and Daniele Callegaro. A software platform for supporting the design and reconfiguration of versatile assembly systems. Procedia CIRP, 72:808–813, 2018. https://doi.org/10.1016/j.procir.2018.03.082
Andreas Hottenrott and Martin Grunow. Flexible layouts for the mixed-model assembly of heterogeneous vehicles. OR Spectrum, 41(4):943–979, 2019. https://doi.org/10.1007/s00291-019-00556-x
Dmitry Ivanov. The industry 5.0 framework: Viability-based integration of the resilience, sustainability, and human centricity perspectives. International Journal of Production Research, 61(5):1683–1695, 2023. https://doi.org/10.1080/00207543.2022.2118892
Anna M Lassila, Sameh M Saad, Terrence Perera, Tomasz Koch, and Jaroslaw Chrobot. Modelling and simulation of humancentred assembly systems-a real case study. In International Conference on Information Technology for Balanced Automation Systems, pages 405–412. Springer, 2004. https://doi.org/10.1007/0-387-22829-2_43
Giovanni Lugaresi and Andrea Matta. Automated manufacturing system discovery and digital twin generation. Journal of Manufacturing Systems, 59:51–66, 2021. https://doi.org/10.1016/j.jmsy.2021.01.005
Giovanni Lugaresi and Andrea Matta. Automated digital twin generation of manufacturing systems with complex material flows: graph model completion. Computers in Industry, 151: 103977, 2023. https://doi.org/10.1016/j.compind.2023.103977
KISS Monika. The future of work: Trends, challenges and potential initiatives. EPRS: European Parliamentary Research Service, 2021.
L´aszl´o Monostori, Botond K´ad´ar, Thomas Bauernhansl, Shinsuke Kondoh, Soundar Kumara, Gunther Reinhart, Olaf Sauer, Gunther Schuh, Wilfried Sihn, and Kenichi Ueda. Cyberphysical systems in manufacturing. Cirp Annals, 65(2): 621–641, 2016. https://doi.org/10.1016/j.cirp.2016.06.005
Venkata Krishna Rao Pabolu and Divya Shrivastava. A dynamic job rotation scheduling conceptual framework by a human representing digital twin. Procedia CIRP, 104:1367–1372, 2021. https://doi.org/10.1016/j.procir.2021.11.230
Venkata Krishna Rao Pabolu, Divya Shrivastava, and Makarand S Kulkarni. A digital-twin based worker’s work allocation framework for a collaborative assembly system. IFACPapersOnLine, 55(10):1887–1892, 2022. https://doi.org/10.1016/j.ifacol.2022.09.674
Mahsa Pourbafrani and Wil MP van der Aalst. Data-driven simulation in process mining: introducing a reference model. Communications of the ECMS, 37(1):411–420, 2023. https://doi.org/10.7148/2023-0411
Lorenzo Ragazzini, N Saporiti, Elisa Negri, Tommaso Rossi, Marco Macchi, Giovanni Pirovano, et al. A digital twinbased approach to the real-time assembly line balancing problem. In Proceedings of the 2nd International Conference on Innovative Intelligent Industrial Production and Logistics, pages 93–99, 2021. https://doi.org/10.5220/0010674500003062
Rom˜ao Santos, Catarina Marques, C´esar Toscano, Hugo M Ferreira, and Joel Ribeiro. Deep reinforcement learning-based approach to dynamically balance multi-manned assembly lines. In International Conference on Flexible Automation and Intelligent Manufacturing, pages 633–640. Springer, 2023. https://doi.org/10.1007/978-3-031-38241-3_71
E Silva, M Donauer, Am´erico Azevedo, P Pec¸as, and E Henriques. A case study evaluating the impact of human behavior on a manufacturing process in-line with automatic processes by means of a simulation model. In 2013 IEEE International Conference on Industrial Engineering and Engineering Management, pages 145–149. IEEE, 2013. https://doi.org/10.1109/ieem.2013.6962392
Fei Tao, Jiangfeng Cheng, Qinglin Qi, Meng Zhang, He Zhang, and Fangyuan Sui. Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 94(9): 3563–3576, 2018. https://doi.org/10.1007/s00170-017-0233-1
Thomas H-J Uhlemann, Christian Lehmann, and Rolf Steinhilper. The digital twin: Realizing the cyber-physical production system for industry 4.0. Procedia Cirp, 61:335–340, 2017. https://doi.org/10.1016/j.procir.2016.11.152
Jan-Frederik Uhlenkamp, Jannicke Baalsrud Hauge, Eike Broda, Michael L¨utjen, Michael Freitag, and Klaus-Dieter Thoben. Digital twins: A maturity model for their classification and evaluation. IEEE Access, 10:69605–69635, 2022. https://doi.org/10.1109/access.2022.3186353
Wil Van Der Aalst and Wil van der Aalst. Data science in action. Springer, 2016. https://doi.org/10.1007/978-3-662-49851-4_1
Wenjun Xu, Zhihao Li, Jiayi Liu, Jia Cui, and Yang Hu. Virtual reconfiguration method of robotic mixed-model assembly line using bees algorithm based on digital twin. IEEE Transactions on Automation Science and Engineering, 2023. https://doi.org/10.1109/tase.2023.3312173
Hyungjoon Yang, Je-Hun Lee, and Hyun-Jung Kim. Assembly line worker assignment and balancing problem with positional constraints. In IFIP International Conference on Advances in Production Management Systems, pages 3–11. Springer, 2021. https://doi.org/10.1007/978-3-030-85914-5_1
Ding Zhang, Jiewu Leng, Min Xie, Hong Yan, and Qiang Liu. Digital twin enabled optimal reconfiguration of the semi-automatic electronic assembly line with frequent changeovers. Robotics and Computer-Integrated Manufacturing, 77:102343, 2022. https://doi.org/10.1016/j.rcim.2022.102343
Rong Zhang, Jianhao Lv, Jinsong Bao, and Yu Zheng. A digital twin-driven flexible scheduling method in a human–machine collaborative workshop based on hierarchical reinforcement learning. Flexible Services and Manufacturing Journal, pages 1–23, 2023. https://doi.org/10.1007/s10696-023-09498-7
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Copyright (c) 2024 Giovanni Lugaresi, Kovacs Laszlo, Kornel Tamas
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