Deployment of Artificial Intelligence in Digital Twin Models for Simulating Industrial Systems and Enhancing Operational Decision-Making via Real-Time Data Synchronization - ijmserh.com

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Article

TITLE	Deployment of Artificial Intelligence in Digital Twin Models for Simulating Industrial Systems and Enhancing Operational Decision-Making via Real-Time Data Synchronization
ABSTRACT	This study investigates the deployment of artificial intelligence (AI) within digital twin (DT) frameworks to simulate industrial systems and improve operational decision-making through real-time data synchronization. Employing a mixed-methods approach, the research integrates simulation modeling, quantitative analysis, and case studies using datasets from manufacturing and energy sectors spanning 2018–2022. Key findings reveal that AI-enhanced DTs reduce unplanned downtime by 32%, improve decision accuracy by 28%, and achieve synchronization latency below 150ms. These outcomes are validated through predictive maintenance models and graph-based synchronization algorithms. The study highlights the transformative role of AI in bridging physical-virtual gaps, while identifying challenges in scalability and legacy system integration. Results underscore implications for Industry 4.0 adoption, offering a replicable framework for data-driven operations. The research contributes to cyber-physical systems theory and provides actionable insights for industrial practitioners aiming to enhance resilience and efficiency in dynamic environments.
AUTHOR	Abhishek Chatrath
PUBLICATION DATE	2026-03-25 12:05:06
VOLUME	11
ISSUE	4
PDF	pdf/2023/10/48_Deployment of Artificial Intelligence in Digital Twin Models for Simulating Industrial Systems and Enhancing Operational Decision-Making via Real-Time Data Synchronization.pdf
KEYWORDS
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