Abstract
Nowadays, global competition in the manufacturing sector is increasingly fierce. Thus, global manufacturing companies must have a manufacturing system that ensures the production of reasonably priced, high-quality products, while meeting the needs of various customers. To address this issue, smart manufacturing should be implemented by adopting various information and communication technologies and convergence with existing manufacturing industries. One of the key technologies required for the implementation of smart manufacturing is a cyber-physical system (CPS). One of the major factors for the successful construction of a CPS is digital twin (DT). In this paper, we propose a standards-based information model for building a DT application, which is a key technology of a CPS-based integrated platform, by overcoming the heterogeneous device environment of global manufacturers and using data collected from various manufacturing sites. Furthermore, we propose a concept of modeling and simulation-based DT application. The DT application proposed in this study facilitates monitoring, diagnosis, and prediction at manufacturing sites using real-time data collected from various environments by ensuring interoperability. Moreover, its validity is verified by applying the technology to a global manufacturing company of automotive parts.
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Acknowledgement
This research was supported by the Ministry of Trade, Industry and Energy (MOTIE) and the Korea Institute for Advancement of Technology (KIAT) in South Korean through the International Cooperative R&D program [P0009839, the Cyber Physical Assembly and Logistics Systems in Global Supply Chains (C-PALS)]. In addition, it was supported by the Ministry of SMEs and Startups through the WC300 Project [S2482274, Development of Multi-vehicle Flexible Manufacturing Platform Technology for Future Smart Automotive Body Production].
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Choi, J. et al. (2021). Design and Implementation of Digital Twin-Based Application for Global Manufacturing Enterprises. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 634. Springer, Cham. https://doi.org/10.1007/978-3-030-85914-5_2
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DOI: https://doi.org/10.1007/978-3-030-85914-5_2
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