The research overview on digital twins (DTs) and their applications in industrial automation, industrial robots, and industrial control systems showcases the progression and integration of DT technology in enhancing the operational efficiency, predictive maintenance, and innovation within Industry 4.0. Here’s a synthesis of the key insights from the literature:
Security Architecture for Industrial Automation and Control Systems:
- Gehrmann and Gunnarsson (2020) discuss a DT-based security architecture that enhances data sharing and control security for industrial processes. They emphasize the role of state synchronization in DTs for secure software upgrades and outline a framework for future research in DT security.
Comprehensive Review on DT Concepts, Technologies, and Applications:
- Liu et al. (2020) provide an extensive review of DTs, covering concepts, key enabling technologies, and applications across various industrial sectors. They highlight the evolution of DT concepts, the significance of integrating domain-specific expertise, and the potential of DTs in optimizing product lifecycle management.
Real-Time Optimization for Industrial Robots:
- Liu et al. (2023) propose a framework utilizing DTs for real-time monitoring and performance optimization of industrial robots. This includes multi-domain modeling, behavioral matching, control optimization, and parameter updating, demonstrating improved control performance through fractional order control.
AI-Driven DTs in Smart Manufacturing and Robotics:
- Huang et al. (2021) survey the integration of DT and AI technologies in smart manufacturing and advanced robotics. They explore applications ranging from metal machining to human–robot interaction, emphasizing the role of AI in enabling sustainable development through DTs.
DT Framework for Collaborative Robotics:
- Malik and Bilberg (2018) present a DT framework to support the design, build, and control of human-machine cooperation in manufacturing. The framework utilizes computer simulations to create a digital counterpart of a collaborative work environment, allowing for continuous improvements and safer embeds.
Challenges and Future Directions for DT Research:
- Fuller et al. (2020) assess the enabling technologies, challenges, and open research areas for DTs, categorizing recent research by areas such as manufacturing, healthcare, and smart cities. They discuss the integration of DTs with AI and IoT technologies, emphasizing the importance of addressing practical challenges to realize the full potential of DTs.
Experimentable DTs for Process Control and Automation:
- Schluse et al. (2018) introduce the concept of experimentable digital twins (EDTs) that bring DTs to life through simulation techniques. EDTs serve as a basis for simulation-based systems engineering, enabling the creation of comprehensive digital representations for process control and automation.
State-of-the-Art in DT for Industry:
- Tao et al. (2019) review the state-of-the-art in DT research, focusing on key components, development, and applications in the industry. They outline the challenges and potential directions for DT research, particularly in enhancing product design, production, and maintenance processes.
This overview highlights the diverse applications and benefits of DT technology in industrial settings, from enhancing security architectures to enabling real-time optimization and facilitating collaborative robotics. The integration of DTs with AI and IoT technologies is a key theme, underscoring the potential of DTs to drive innovation and efficiency in Industry 4.0.
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