Abstract
Digital twinning is one of the top ten technology trends in the last couple of years, due to its high applicability in the industrial sector. The integration of big data analytics and artificial intelligence/machine learning (AI-ML) techniques with digital twinning, further enriches its significance and research potential with new opportunities and unique challenges. To date, a number of scientific models have been designed and implemented related to this evolving topic. However, there is no systematic review of digital twinning, particularly focusing on the role of AI-ML and big data, to guide the academia and industry towards future developments. Therefore, this article emphasizes the role of big data and AI-ML in the creation of digital twins (DTs) or DT-based systems for various industrial applications, by highlighting the current state-of-the-art deployments. We performed a systematic review on top of multidisciplinary electronic bibliographic databases, in addition to existing patents in the field. Also, we identified development-tools that can facilitate various levels of the digital twinning. Further, we designed a big data driven and AI-enriched reference architecture that leads developers to a complete DT-enabled system. Finally, we highlighted the research potential of AI-ML for digital twinning by unveiling challenges and current opportunities.
Highlights
Digital twinning is a process that involves the creation of a virtual model of any physical object, in order to streamline, optimize, and maintain the underlying physical process
The authors demonstrated the effectiveness of their framework with a case study of cooperative ramp merging involving three passenger vehicles, and the results showed that the digital twin can assist transportation systems
CURRENT DEPLOYMENTS OF DIGITAL TWINS USING BIG DATA AND MACHINE LEARNING We have identified the primary sectors where digital twins (DTs)-based systems are developed with the help of artificial intelligence/machine learning (AI-ML) techniques
Summary
Digital twinning is a process that involves the creation of a virtual model (i.e., a twin) of any physical object, in order to streamline, optimize, and maintain the underlying physical process. Rathore et al.: Role of AI, Machine Learning, and Big Data in Digital Twinning: A SLR, Challenges, and Opportunities monitoring of resources, and increasing the life of the product by predicting product failure. There is an exigency of a systematic approach towards the thorough review of the current developments in AI-enabled digital twinning using IoT technology and big data. This can drive both academia and industry towards further research, by highlighting the current findings, future potentials, challenges, and applications of AI-enabled digital twinning in the industrial sector. We highlighted the role of big data, AI, machine learning, and IoT technologies in the process of digital twin creation, by listing examples from current deployments in various industrial domains.
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