Digital Twins in Manufacturing: A Survey of Current Practices and Future Trends

Abhinav Parashar A Singh * and Neepakumari Gameti

Independent Researcher.
 
Review
International Journal of Science and Research Archive, 2024, 13(01), 1240–1250.
Article DOI: 10.30574/ijsra.2024.13.1.1705
Publication history: 
Received on 04 August 2024; revised on 11 September 2024; accepted on 14 September 2024
 
Abstract: 
Digital Twin (DT) is the advanced tool of smart manufacturing which provides a proper way to model, analyse, and control the physical systems. A DT is the lifelike rendition of a physical object or phenomenon that uses real-time actual phenomena data to depict its actions, efficiency, and responses within the surrounding context. This technology allows electronic manufacturers to obtain a real-time stream of their systems, in which predictive maintenance, increased operation effectiveness, and improved product quality can be attained. By developing an ever-evolving replica of the physical structures, systems or even operation lines, Digital Twins enable the functionality of exploring various demanding circumstances and schemata, all in order to make effective decisions. When the manufacturing environments are rapidly changing, and there are more interconnections between them, the use of Digital Twins is especially useful. They continue improvement by highlighting problems, forecasting failure, and making choices based on data. The purpose of this article is to examine smart manufacturing via the lens of IoT DT technology. It gives general information about its uses, such as in the processes, time-saving and safety measures. As well, the paper considers the difficulties related to the practical application of DT, including costs, data management, and requirements for the formation of standards. Thus, based on analysing future developments and the role of Digital Twins as an innovation enabler in different industries, this study can benefit scholars, practitioners, and politicians who look forward to unlocking the potential of this innovative solution.
 
Keywords: 
Digital Twin; Smart Manufacturing; Real-Time Data; Predictive Maintenance; Process Optimization; Manufacturing Efficiency
 
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0