Data enabling technology in digital twin and its frameworks in different industrial applications

Abstract

Digital twins (DT) are virtual representations of physical entities that integrate real-time data and simulation. By mirroring real-world counterparts and continuously updating based on live data, DTs allow organizations to simulate, monitor, and control processes with unprecedented precision, thus reducing costs, improving productivity, facilitating innovation and adaptability in industrial operations. Studying DT technology is critical to address the growing complexity of industrial systems and the need for more adaptable, efficient data integration of multisource, secure data enabling technologies and frameworks. The study highlighted the pivotal role of DT technology in advancing industrial digitalization, particularly within the manufacturing sector. It examined the origins, evolution, and potential applications of DTs, incorporating insights from academic and industrial perspectives. By reviewing a range of literatures, this article identifies gaps in advancing DT technology in smart manufacturing systems in terms of technical limitations hampering the implementation, emphasizing the need for more adaptable and accessible frameworks, integrating multisource data, ensuring scalability, and maintaining data security to meet the evolving demands of Industry 4.0 with better efficiency and reduce costs. The findings underscored the necessity for continued research and development to establish adaptable and robust DT technologies which provide scalable architectures, improved interoperability, and enhanced accuracy in simulations, capable of meeting the current evolving industrial demands.