Development of a Digital Twin Framework for Predictive Maintenance in Smart Manufacturing Environments

Author(s)	Mr. Sangamesh Ramesh Yankanchi, Mr. Shreyas S, Mr. Kiran S
Country	India
Abstract	Digital Twin (DT) technology is revolutionizing predictive maintenance (PdM) by creating real-time virtual models of physical assets, enabling proactive failure detection, maintenance optimization, and reduced downtime across industries such as manufacturing, aerospace, and energy. This study reviews 98 studies on DT-enabled PdM, examining its applications, key frameworks, and challenges. Platforms such as Smart Factory Digital Twin (SFDT) and Digital Twin-Industrial Internet (DTII) illustrate the way in which DTs incorporate IoT and machine learning (ML) for predictive accuracy and operational resilience. Yet, high computational needs, security of data, and interoperability restrict broad implementation. Solutions such as hybrid and cognitive DTs are emerging that hold potential for versatile, scalable DT systems. This research identifies DTs’ capability to advance PdM in Industry 4.0 and suggests future studies for advancing ML integration, standardization, and security within DT frameworks
Keywords	Digital Twin, Predictive Analytics, Digital supply chain twin, Artificial intelligence
Field	Engineering
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-06-03
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.46785
Short DOI	https://doi.org/g9m275

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International Journal For Multidisciplinary Research