DIGITAL TWIN AND MACHINE LEARNING
International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)
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Abstract
The integration of Digital Twin (DT) technology with Machine Learning (ML) has emerged as a powerful combination to enhance the performance, predictive capabilities, and operational efficiency of various systems across industries. A Digital Twin is a virtual replica of physical entities or processes, allowing real-time monitoring, simulation, and optimization. When coupled with ML algorithms, Digital Twins can evolve by learning from the data generated by their physical counterparts, thereby improving decision-making, predictive maintenance, and resource optimization The combination of DT and ML has proven to be transformative in sectors such as manufacturing, healthcare, automotive, and smart cities, where it facilitates enhanced diagnostics, anomaly detection, and scenario-based simulations. However, challenges related to data quality, real-time processing, and the scalability of solutions remain. This paper explores the key concepts, applications, and future potential of Digital Twin and Machine Learning, discussing their impact on industries and presenting a roadmap for overcoming existing challenges to fully realize their capabilities.
References
1) Tao,F.,Zhang,M.,&Liu,Y.(2019).Digital Twin and Cyber-Physical Systems: A New Paradigm for Smart Manufacturing and Systems Engineering.
2) Wang,L.,etal.(2021).A Survey of Machine Learning Techniques for Digital Twin in Industry 4.0 Journal of Manufacturing Systems, 60, 175–186.
3) Fuller,A.,Fan,Z.,& Day,C.(2020).Digital Twin: Enabling the Next Generation of Manufacturing and Smart Systems CIRP Annals, 69(2), 637–660.
4) McKinsey & Company.(2021). The Future of Manufacturing: How Digital Twins Are Reshaping the Industry McKinsey & Company.
5) Deloitte. (2021).Digital Twin in the Manufacturing Industry: The Next Frontier of Data-Driven Innovation Deloitte Insight.
Keywords
Digital Twin (DT) - Real-Time Monitoring - Predictive Modelling - Cyber- Physical Systems (CPS).
