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Data Analytics and Visualization for Unveiling Insights from Digital Twins: Trends and Challenges
Abstract
igital twins represent a paradigm shift in engineering analytics and simulations. As virtual replicas of physical assets and processes, digital twins require the confluence of massive amounts of data, predictive analytics, and visualization capabilities to unlock their full potential. This study provides a comprehensive overview of the role of data analytics and visualization techniques in extracting value from digital twin data. An in-depth literature review analyzes current research trends and developments in this emerging field. The methodological framework, results, and key findings are presented from an empirical study on the application of machine learning, statistical modeling, and interactive visualizations to generate insights from digital twin data in manufacturing systems. Core challenges related to scalability, uncertainty quantification, and dimensionality reduction are discussed. The study findings demonstrate the pivotal role data analytics and visualization play in creating actionable intelligence from digital twins. This study concludes with an outlook on promising directions for future research at the nexus of data science, visualization, and digital twin technologies.
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