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Application of IoT-Aided Simulation to Manufacturing Systems in Cyber-Physical System

1
Faculty of Commerce, Chuo Gakuin University, 451 Kujike, Abiko, Chiba 270-1196, Japan
2
Department of Industrial and Systems Engineering, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan
*
Author to whom correspondence should be addressed.
Machines 2019, 7(1), 2; https://doi.org/10.3390/machines7010002
Received: 26 October 2018 / Revised: 15 December 2018 / Accepted: 17 December 2018 / Published: 3 January 2019
(This article belongs to the Special Issue Smart Manufacturing)
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Abstract

With the rapid development of mobile and wireless networking technologies, data has become more ubiquitous and the IoT (Internet of Things) is attracting much attention due to high expectations for enabling innovative service, efficiency, and productivity improvement. In next-generation manufacturing, the digital twin (DT) has been proposed as a new concept and simulation tool for collecting and synchronizing real-world information in real time in cyber space to cope with the challenges of smart factories. Although the DT is considered a challenging technology, it is still at the conceptual stage and only a few studies have specifically discussed methods for its construction and implementation. In this study, we first explain the concept of DT and important issues involved in developing it within an IoT-aided manufacturing environment. Then, we propose a DT construction framework and scheme for inputting data derived from the IoT into a simulation model. Finally, we describe how we verify the effectiveness of the proposed framework and scheme, by constructing a DT-oriented simulation model for an IoT-aided manufacturing system. View Full-Text
Keywords: IoT; cyber-physical systems; digital twin; simulation approach; smart factory IoT; cyber-physical systems; digital twin; simulation approach; smart factory
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Tan, Y.; Yang, W.; Yoshida, K.; Takakuwa, S. Application of IoT-Aided Simulation to Manufacturing Systems in Cyber-Physical System. Machines 2019, 7, 2.

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