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http://dx.doi.org/10.22156/CS4SMB.2021.11.04.019

Experimental Implementation of Digital Twin Simulation for Physical System Optimization  

Kim, Kyung-Ihl (Division of Convergence Management, Korea National University of Transportation)
Publication Information
Journal of Convergence for Information Technology / v.11, no.4, 2021 , pp. 19-25 More about this Journal
Abstract
This study proposes a digital twin implementation method through simulation so that the manufacturing process can be optimized in a manual manufacturing site. The scope of the proposal is a knowledge management mechanism that collects manual motion with a sensor and optimizes the manufacturing process with repetitive experimental data for motion recognition. In order to achieve the research purpose, a simulation of the distribution site was conducted, and a plan to create an optimized digital twin was prepared by repeatedly experiencing the work simulation based on the basic knowledge expressed by the worker's experience. As a result of the experiment, it was found that it is possible to continuously improve the manufacturing process by transmitting the result of configuring the optimized resources to the physical system by generating the characteristics of the work space configuration and working step within a faster time with the simulation that creates the digital twin.
Keywords
Digital Twin; Sensor data; Cyber Physical System; Knowledge management mechanisim; Simulation;
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1 J. Min & J. Chai. (2012). Motion Graphs++: A Compact Generative Model for Semantic Motion Analysis and Synthesis. ACM Transactions on Graphics, 31(6), 12. DOI : 10.1145/2366145.2366172.   DOI
2 G. Pintzos, N. Nikolakis, K. Alexopoulos & G. Chryssolouris. (2016). Motion Parameters Identification for the Authoring of Manual Tasks in Digital Human Simulations: An Approach Using Semantic Modelling. Procedia CIRP. 41. 752-758. DOI : 10.1016/j.procir.2015.12.077.   DOI
3 M. Manns & N. A. A. Martin. (2015). Improving A Walk Trajectories with B-Splines and Motion Capture for Manual Assembly Verification. Procedia CIRP. 33. 364-369. DOI : 10.1016/j.procir.2015.06.083.   DOI
4 E. Herrmann, M. Manns, H. Du, S. Hosseini & K. Fischer. (2017). Accelerating Statistical Human Motion Synthesis Using Space Partitioning Data Structures. Computer animation & Virtual worlds. 28. e1780. DOI : 10.1002/cav.1780.   DOI
5 F. Tao & M. Zhang. (2017). Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing. IEEE Access 5: 20418-20427. DOI : 10.1109/ACCESS.2017.2756069.   DOI
6 B. G. Thiede, S. Posselt , S. Kauffeld & C. Herrmann. (2017). Enhancing Learning Experience in Physical Action-Orientated Learning Factories Using a Virtually Extended Environment and Serious Gaming Approaches. Procedia Manufacturing, 9, 238-244. DOI : 10.1016/j.promfg.2017.04.042.   DOI
7 D. M. Battini, F. Faccio, A. Persona & F. Sgarbossa. (2011). New Methodological Framework to Improve Productivity and Ergonomics in Assembly System Design." International Journal of Industrial Ergonomics, 41(1), 30-42. DOI : 10.1016/j.ergon.2010.12.001.   DOI
8 Y. Lu, T. Peng & X. Xu. (2019). Energy-efficient cyber-physical production network: architecture and technologies, Computing and Industrial Engineering. 129, 56-66. DOI : 10.1016/j.cie.2019.01.025.   DOI
9 M. Schluse, M. Priggemeyer, L. Atorf & J. Rossmann. (2018). Experimentable digital twins streamlining simulation-based systems engineering for industry 4.0, IEEE Trans. Industrial Informations. 14. 1722-1731. DOI : 10.1109/TII.2018.2804917.   DOI
10 Blender. (2015). Home of the Blender project - Free and Open 3D Creation Software. (Online). https://www.blender.org/
11 M. Rietzler, F. Geiselhart, J. Thomas, & E. Rukzio. (2016). FusionKit: A Generic Toolkit for Skeleton, Marker and Rigid-Body Tracking. In Proceedings of the 8th ACM SIGCHI Symposium on Engineering Interactive Computing Systems - EICS '16, 73-84. New York, USA: ACM. DOI : 10.1145/2933242.2933263   DOI
12 D. Park, M. Choi & D. Yang, (2020), A Study on a framework for digital Twin management system applicable to smart factory, Journal of convergence for information technology, 10(9), 1-7. DOI : 10.22156/CS4SMB.2020.10.09.001.   DOI
13 S. Herath, M. Harandi & F. Porikli. (2017). Going Deeper into Action Recognition: A Survey. Journal of Image and Vision Computing. 60. 4-21. DOI : 10.1016/j.imavis.2017.01.010.   DOI