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AI-Driven Digital Twins for Manufacturing: A Review Across Hierarchical Manufacturing System Levels
Phat Nguyen1, Minjung Kim1, Elaina Nichols1
1Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA.
Sensors (Basel, Switzerland)
|January 10, 2026
Summary
Digital Twins (DTs) enhanced with Artificial Intelligence (AI) are revolutionizing smart manufacturing. AI-driven DTs enable real-time optimization and autonomous control, transforming industries from machine to enterprise levels.
Area of Science:
- Manufacturing Technology
- Artificial Intelligence
- Digital Systems
Background:
- Digital Twins (DTs) initially served as advanced simulation models.
- The integration of Artificial Intelligence (AI) has significantly evolved DT capabilities.
- AI enhances DTs for knowledge acquisition, optimization, and autonomous control.
Purpose of the Study:
- To provide a state-of-the-art review of AI-driven DTs in manufacturing.
- To highlight key applications, challenges, and future research directions.
- To structure the evolution and scalability of AI-driven DTs across integration levels.
Main Methods:
- Literature survey of AI-driven DTs in manufacturing.
- Analysis of AI techniques like deep reinforcement learning and CNNs.
- Categorization of case studies by integration level: machine, cell, shop floor, and enterprise.
Main Results:
- AI-driven DTs enable predictive maintenance, process optimization, quality control, and dynamic scheduling.
- Successful deployments in CNC machining, robotics, and industrial printing show improvements in efficiency and reliability.
- Scalability demonstrated from individual assets to interconnected manufacturing ecosystems.
Conclusions:
- AI-driven DTs are transforming manufacturing into intelligent, adaptive systems.
- High-fidelity real-time data and seamless physical-digital alignment are crucial for intelligent DTs.
- Future research will focus on further enhancing the capabilities and integration of AI-driven DTs.
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