American International Journal of Research and Innovation

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AI-Enabled Digital Twins for Industrial Performance Optimization

Author(s) Dr. Jurjen Helmus
Country United States
Abstract The rapid advancement of artificial intelligence, industrial automation, and cyber-physical technologies has accelerated the development of intelligent manufacturing environments. Modern industries increasingly require advanced computational approaches capable of improving operational efficiency, predictive maintenance, resource utilization, and production optimization. Traditional industrial monitoring and management systems often depend on historical analysis and reactive decision-making processes, which may limit their ability to address dynamic operational challenges. AI-enabled Digital Twins have emerged as transformative technologies that integrate artificial intelligence with virtual industrial representations to enable real-time monitoring, predictive analysis, and intelligent optimization.
This study examines AI-enabled Digital Twins for industrial performance optimization by analysing their contribution to operational efficiency, predictive maintenance, production intelligence, and resource management. The research explores how artificial intelligence techniques, including machine learning, predictive analytics, and intelligent simulation models, enhance the capabilities of digital twin environments. A simulation-based analytical framework is developed to compare conventional industrial management approaches with AI-driven digital twin systems using evaluation indicators including operational efficiency, predictive accuracy, maintenance optimization, and resource utilization effectiveness.
The findings demonstrate that AI-enabled Digital Twins significantly improve industrial performance by providing real-time insights, predictive capabilities, and adaptive optimization mechanisms. The results indicate that intelligent digital twin systems support proactive decision-making, reduce operational inefficiencies, and enhance industrial sustainability. However, successful implementation requires addressing challenges related to data integration, computational complexity, cybersecurity, scalability, and technological infrastructure.
The study contributes to emerging research on intelligent industrial systems by presenting AI-enabled Digital Twins as strategic frameworks for next-generation manufacturing and industrial optimization. The integration of artificial intelligence with virtual industrial models provides opportunities for developing adaptive, efficient, and sustainable industrial ecosystems.
Keywords Artificial Intelligence, Digital Twins, Industrial Optimization, Smart Manufacturing, Predictive Maintenance, Intelligent Systems, Industry 4.0.
Field Engineering
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-15
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