Abstract
In Recent times, Digital Twin (DT) technology has emerged as an innovative tool for the enhancement of the efficiency and reliability of manufacturing systems, through the creation of virtual replicas of physical assets, systems and processes. As vibrations in machines and tools can lead to reduced product quality, increased wear and tear, as well as unplanned downtimes, this article explores the application of digital twins for predicting and controlling vibrations in manufacturing systems. The integration of digital twins assists in addressing these challenges through the provision of insights into vibration dynamics, enabling proactive maintenance, and optimizing system performance. It examines how sensor data and advanced computational models converge to simulate and predict vibration behavior in real time. Moreover, the role of artificial intelligence and machine learning in analyzing vibration patterns and prescribing corrective measures is discussed. The article also presents case studies that highlight successful implementations of digital twins in diverse manufacturing contexts, showcasing measurable improvements in productivity and system reliability. In addition, the research identifies key challenges, including data integration complexities, high computational requirements, and cost implications, that manufacturers must address to fully leverage the potential of digital twins. By synthesizing these insights, the paper provides a comprehensive framework for researchers and practitioners seeking to harness digital twin technology for vibration management in manufacturing environments.
Published Version
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