A Simple Node-Red Implementation for Digital Twins in the Area of Manufacturing

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  • Blessing Ngonidzashe Musungate Yaşar University
  • Ahmet Tuncay Ercan Yaşar University




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.


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Author Biographies

Blessing Ngonidzashe Musungate, Yaşar University

Department of Computer Engineering,  Turkiye

Ahmet Tuncay Ercan, Yaşar University

Department of Management Information Systems,Turkiye


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How to Cite

Musungate, B. N., & Ercan, A. T. (2023). A Simple Node-Red Implementation for Digital Twins in the Area of Manufacturing. International Journal of Advanced Natural Sciences and Engineering Researches, 7(9), 24–30. https://doi.org/10.59287/ijanser.1497

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