Digital twins, virtual replicas synchronized with physical objects or systems, are revolutionizing industries. Utilizing real-time data and simulation technologies, they enable predictive maintenance, optimize operations, and inform decision-making, driving efficiency and innovation across sectors.
3. 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:
1. 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
.
2. 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
.
3. 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
.
4. 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
.
5. 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
.
6. 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
4. 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
.
7. 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
.
8. 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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