A Simple Node-Red Implementation for Digital Twins in the Area of Manufacturing
Abstract views: 67 / PDF downloads: 23
Keywords:Digital Twin, Industrial Internet of Things, Iiot, Node-RED, Predictive Maintenance, Machine Learning
Interest in digital twins continues to strengthen with technological advancements in Industrial IoT. A digital twin is a virtual representation that models a physical object and effectively provides a twoway interaction with the real system. Digital twin models can be set up to test or analyze industrial applications before deployment thereby improving the efficiency of industries. In this work, a Node-RED implementation for digital twins in the manufacturing sector is developed. Plastic injection molding is the chosen case study for the implementation of this digital twin. Node-RED is a platform that allows developers to quickly build Internet of Things applications using a simple web browser interface. The digital twin uses the Random Forest Classifier algorithm to do predictive maintenance tasks including classification of quality of products. An easy-to-use dashboard is developed to enable the user to interact with the digital twin. Important modules such as communication with the real environment, SMS, and email notifications are successfully implemented in the digital twin. A windows application is used to mimic the real environment that communicates with the digital twin. The findings show that it is feasible to build a Node-RED digital twin for an industrial platform. The flexibility of Node-RED makes it suitable for building architecture of varying complexity.
M. Grieves and J. Vickers, “Digital Twin : Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems (Excerpt)”,: 10.13140/RG.2.2.26367.61609,Aug 2016
X. Fang, H. Wang, G. Liu, X. Tian, G. Ding, and H. Zhang, “Industry application of digital twin: from concept to implementation,” The International Journal of Advanced Manufacturing Technology, vol. 121, no. 7–8, pp. 4289–4312, Jul. 2022, doi: 10.1007/s00170-022-09632-z.
M. Singh, E. Fuenmayor, E. Hinchy, Y. Qiao, N. Murray, and D. Devine, “Digital Twin: Origin to Future,” Applied System Innovation, vol. 4, no. 2, p. 36, May 2021, doi: 10.3390/asi4020036.
https://nodered.org/ (Accessed 25.04.2023)
https://flows.nodered.org/ (Accessed 25.04.2023)
https://www.npmjs.com/ (Accessed 20.04.2023)
https://github.com/mljs/ (Accessed 27.04.2023)
L. Bogedale, S. Doerfel, A. Schrodt, and H.-P. Heim, “Online Prediction of Molded Part Quality in the Injection Molding Process Using High-Resolution Time Series,” Polymers, vol. 15, no. 4, p. 978, Feb. 2023, doi: 10.3390/polym15040978.
G. Aslantaş , M. Özsaraç , M. Rumelli , T. Alaygut , G. Bakırlı and D. Bırant , "Prediction of Remaining Useful Life for Plastic Injection Molding Machines Using Artificial Intelligence Methods", Journal of Artificial Intelligence and Data Science, vol. 2, no. 1, pp. 8-15,
B.N. Musungate and A.T. Ercan, “Edge computingbased predictive maintenance middleware for Industrial IoT”. Bursa 4. International Conference on Scientific Researches [Bursa],p86-98, Jul.2023.
A. Polenta, S. Tomassini, N. Falcionelli, P. Contardo, A. F. Dragoni, and P. Sernani, “A Comparison of Machine Learning Techniques for the Quality Classification of Molded Products,” Information, vol. 13, no. 6, p. 272, May 2022, doi: 10.3390/info13060272.
C. Steinmetz et al., “Digital Twins modeling and simulation with Node-RED and Carla,” IFACPapersOnLine, vol. 55, no. 19, pp. 97–102, 2022, doi: 10.1016/j.ifacol.2022.09.190.
A. Yasin, T. Y. Pang, C.-T. Cheng, and M. Miletic, “A Roadmap to Integrate Digital Twins for Small and Medium-Sized Enterprises,” Applied Sciences, vol. 11, no. 20, p. 9479, Oct. 2021, doi: 10.3390/app11209479
L. Messi, B. Naticchia, A. Carbonari, L. Ridolfi, G. Martino and D. Giuda, “Development of a Digital Twin Model for Real-Time Assessment of Collision Hazards”.p14–19,2022
A. Lind, A. Syberfeldt and L. Hanson, “Evaluating a Digital Twin Concept for an Automatic Up-to-Date Factory Layout Setup”.