Abstract
In response to the development trend of unmanned and intelligent weapons and equipment for the future, intelligent digital equipment, as a “digital brain”, can be deeply integrated into ...various links such as equipment development and upgrading, experimental verification, and operational support, to lead to leapfrog development of the operational support for future intelligent equipment. First, intelligent digital equipment allows the physical equipment to make adaptive adjustments and boost operational efficiency during combat. Second, it helps the equipment build a capability of perceiving its status and thus improves the operational readiness of equipment in normal times. Third, it allows equipment fault isolation for diagnosis and thus improves the completion rate of missions in combat. Fourth, it provides intelligent guidance for operation training and enhances the efficiency of use and training. Fifth, it achieves online upgrading and verification of functionalities, and changes and upgrades the maintenance mode. This paper introduces the concept of digital equipment and the research outcomes concerning digital twin around the world from 2010 to 2020, and reviews the research progress in the United States and China. It presents the problems faced by the research, and puts forward solutions and research prospects.
Cyber-physical system (CPS) is a new trend in the Internet-of-Things related research works, where physical systems act as the sensors to collect real-world information and communicate them to the ...computation modules (i.e. cyber layer), which further analyze and notify the findings to the corresponding physical systems through a feedback loop. Contemporary researchers recommend integrating cloud technologies in the CPS cyber layer to ensure the scalability of storage, computation, and cross domain communication capabilities. Though there exist a few descriptive models of the cloud-based CPS architecture, it is important to analytically describe the key CPS properties: computation, control, and communication. In this paper, we present a digital twin architecture reference model for the cloud-based CPS, C2PS, where we analytically describe the key properties of the C2PS. The model helps in identifying various degrees of basic and hybrid computation-interaction modes in this paradigm. We have designed C2PS smart interaction controller using a Bayesian belief network, so that the system dynamically considers current contexts. The composition of fuzzy rule base with the Bayes network further enables the system with reconfiguration capability. We also describe analytically, how C2PS subsystem communications can generate even more complex system-of-systems. Later, we present a telematics-based prototype driving assistance application for the vehicular domain of C2PS, VCPS, to demonstrate the efficacy of the architecture reference model.
As electric vehicle adoption accelerates and demand increases, the inability to produce batteries in sufficient quantities has emerged as a critical bottleneck in the electric vehicle supply chain. ...Given the impending climate change crisis, resolving this bottleneck is imperative to accelerate the transition to a zero-emission electric mobility future. One potential solution is the use of robotics for fast and cost-effective assembly of batteries at scale. This study proposes a three-stage digital twin design and analysis method to develop robotic workcells for fast and cost-effective assembly of electric vehicle battery modules. Using digital twin design and simulation, robotic assembly line configurations have been developed for battery module production at different scales. Digital twin analytics was used to evaluate and optimise the proposed robotic battery assembly system for speed and cost. Industrial automation experts were consulted to further improve robotic work cell layouts to minimise investment in robots. Because digital twins of robotic workcells have been used, the configurations of the battery assembly line, as designed and validated, are ready for immediate implementation. For practitioners, this study offers heuristic methods to determine the appropriate assembly line configuration, the required number of robots and humans, for a desired production volume. For researchers, this study outlines promising areas for future investigation.
Industry 4.0, cyber-physical production systems (CPPS) and the Internet of Things (IoT) are current focusses in automation and data exchange in manufacturing, arising from the rapid increase in ...capabilities in information and communication technologies and the ubiquitous internet. A key enabler for the advances promised by CPPSs is the concept of a
digital twin
, which is the virtual representation of a real-world entity, or the
physical twin
. An important step towards the success of Industry 4.0 is the establishment of practical reference architectures. This paper presents an architecture for such a digital twin, which enables the exchange of data and information between a remote emulation or simulation and the physical twin. The architecture comprises different layers, including a local data layer, an IoT Gateway layer, cloud-based databases and a layer containing emulations and simulations. The architecture can be implemented in new and legacy production facilities, with a minimal disruption of current installations. This architecture provides a service-based and real-time enabled infrastructure for vertical and horizontal integration. To evaluate the architecture, it was implemented for a small, but typical, physical manufacturing system component.
•An image segmentation model is built on Memory-augmented Neural Networks (MANNs), in an effort to identify the ever-boosting image information with more details.•Results demonstrate that the ...MANNs-based image segmentation model is more accurate and•consumes less training time than other classic models.
With the continuous increase of the amount of information, people urgently need to identify the information in the image in more detail in order to obtain richer information from the image. This work explores the dynamic complex image segmentation of self-driving vehicle under Digital Twins (DTs) based on Memory-augmented Neural Networks (MANNs), so as to further improve the performance of self-driving in intelligent transportation. In view of the complexity of the environment and the dynamic changes of the scene in intelligent transportation, this work constructs a segmentation model for dynamic complex image of self-driving vehicle under DTs based on MANNs by optimizing the Deep Learning algorithm and further combining with the DTs technology, so as to recognize the information in the environment image during the self-driving. Finally, the performance of the constructed model is analyzed by experimenting with different image datasets (PASCALVOC 2012, NYUDv2, PASCAL CONTEXT, and real self-driving complex traffic image data). The results show that compared with other classical algorithms, the established MANN-based model has an accuracy of about 85.80%, the training time is shortened to 107.00 s, the test time is 0.70 s, and the speedup ratio is high. In addition, the average algorithm parameter of the given energy function α=0.06 reaches the maximum value. Therefore, it is found that the proposed model shows high accuracy and short training time, which can provide experimental reference for future image visual computing and intelligent information processing.
•EtherTwin, a blockchain-based Decentralized Application (DApp) for secure information management of Industry 4.0 assets using Digital Twins.•Secure information management, ensuring confidentiality ...through fine-grained access control and encryption, as well as providing integrity and availability based on the blockchain.•Quantitative and qualitative evaluation including performance/cost measurements as well as a real-world industry use case and expert interviews.•Full-featured open source prototype EtherTwin based on blockchain design patterns and state-of-the-art DApp technologies (Ethereum, Swarm).
Digital Twins are complex digital representations of assets that are used by a variety of organizations across the Industry 4.0 value chain. As the digitization of industrial processes advances, Digital Twins will become widespread. As a result, there is a need to develop new secure data sharing models for a complex ecosystem of interacting Digital Twins and lifecycle parties. Decentralized Applications are uniquely suited to address these sharing challenges while ensuring availability, integrity and confidentiality. They rely on distributed ledgers and decentralized databases for data storage and processing, avoiding single points of trust. To tackle the need for decentralized sharing of Digital Twin data, this work proposes an owner-centric decentralized sharing model. A formal access control model addresses integrity and confidentiality aspects based on Digital Twin components and lifecycle requirements. With our prototypical implementation EtherTwin we show how to overcome the numerous implementation challenges associated with fully decentralized data sharing, enabling management of Digital Twin components and their associated information. For validation, the prototype is evaluated based on an industry use case and semi-structured expert interviews.
Future wireless services will focus on improving the quality of life by enabling various applications, such as extended reality, brain-computer interaction, and healthcare. These applications will ...have diverse performance requirements (e.g., user-defined quality of experience metrics, latency, and reliability) which will be challenging to be fulfilled by existing wireless systems. To meet the diverse requirements of the emerging applications, the concept of digital twins has been recently proposed. A digital twin uses a virtual representation along with security-related technologies (e.g., blockchain), communication technologies (e.g., 6G), computing technologies (e.g., edge computing), and machine learning, so as to enable the smart applications. In this tutorial, we present a comprehensive overview on digital twins for wireless systems. First, we present the fundamental concepts (i.e., design aspects, high-level architecture, and frameworks) of digital twins for wireless systems. Second, a comprehensive taxonomy is devised for two aspects, namely, twins for wireless and wireless for twins. For the twins for wireless aspect, we consider issues related to design of twin objects, physical devices, and interface, as well as prototyping, deployment trends, incentive mechanism, isolation of twins, and decoupling. For the wireless for twins aspect, we consider issues related to accessing twin objects, security and privacy, and air interface design are considered. Finally, open research challenges and opportunities are discussed.
Emerging technologies, such as mobile-edge computing (MEC) and next-generation communications are crucial for enabling rapid development and deployment of the Internet of Things (IoT). With the ...increasing scale of IoT networks, how to optimize the network and allocate the limited resources to provide high-quality services remains a major concern. The existing work in this direction mainly relies on models that are of less practical value for resource-limited IoT networks, and can hardly simulate the dynamic systems in real time. In this article, we integrate digital twins with edge networks and propose the digital twin edge networks (DITENs) to fill the gap between physical edge networks and digital systems. Then, we propose a blockchain-empowered federated learning scheme to strengthen communication security and data privacy protection in DITEN. Furthermore, to improve the efficiency of the integrated scheme, we propose an asynchronous aggregation scheme and use digital twin empowered reinforcement learning to schedule relaying users and allocate spectrum resources. Theoretical analysis and numerical results confirm that the proposed scheme can considerably enhance both communication efficiency and data security for IoT applications.
The teleoperation and coordination of multiple industrial robots play an important role in today’s industrial internet-based collaborative manufacturing systems. The user-friendly teleoperation ...approach allows operators from different manufacturing domains to reduce redundant learning costs and intuitively control the robot in advance. Nevertheless, only a few preliminary works have been introduced very recently, let alone its effective implementation in the manufacturing scenarios. To address the gap, this research proposes a novel multi-robot collaborative manufacturing system with human-in-the-loop control by leveraging the cutting-edge augmented reality (AR) and digital twin (DT) techniques. In the proposed system, the DTs of industrial robots are firstly mapped to physical robots and visualize them in the AR glasses. Meanwhile, a multi-robot communication mechanism is designed and implemented, to synchronize the state of robots in the twin. Moreover, a reinforcement learning algorithm is integrated into the robot motion planning to replace the conventional kinematics-based robot movement with corresponding target positions. Finally, three interactive AR-assisted DT modes, including real-time motion control, planned motion control, and robot monitoring mode are generated, which can be readily switched by human operators. Two experimental studies are conducted on (1) a single robot with a commonly used peg-in-hole experiment, and (2) the motion planning of multi-robot collaborative tasks, respectively. From the experimental results, it can be found that the proposed system can well handle the multi-robot teleoperation tasks with high efficiency and owns great potentials to be adopted in other complicated manufacturing scenarios in the near future.
•Presented a multi-robot multi-client-based communication mechanism to synchronize teleoperation states.•Introduced three typical augmented reality-assisted digital twin-enabled human-in-the-loop robot control modes.•Proposed a RL algorithm for motion control and planning of the multi-robots with learning intelligence.
Digital Twin: Origin to Future Singh, Maulshree; Fuenmayor, Evert; Hinchy, Eoin ...
Applied system innovation,
05/2021, Volume:
4, Issue:
2
Journal Article
Peer reviewed
Open access
Digital Twin (DT) refers to the virtual copy or model of any physical entity (physical twin) both of which are interconnected via exchange of data in real time. Conceptually, a DT mimics the state of ...its physical twin in real time and vice versa. Application of DT includes real-time monitoring, designing/planning, optimization, maintenance, remote access, etc. Its implementation is expected to grow exponentially in the coming decades. The advent of Industry 4.0 has brought complex industrial systems that are more autonomous, smart, and highly interconnected. These systems generate considerable amounts of data useful for several applications such as improving performance, predictive maintenance, training, etc. A sudden influx in the number of publications related to ‘Digital Twin’ has led to confusion between different terminologies related to the digitalization of industries. Another problem that has arisen due to the growing popularity of DT is a lack of consensus on the description of DT as well as so many different types of DT, which adds to the confusion. This paper intends to consolidate the different types of DT and different definitions of DT throughout the literature for easy identification of DT from the rest of the complimentary terms such as ‘product avatar’, ‘digital thread’, ‘digital model’, and ‘digital shadow’. The paper looks at the concept of DT since its inception to its predicted future to realize the value it can bring to certain sectors. Understanding the characteristics and types of DT while weighing its pros and cons is essential for any researcher, business, or sector before investing in the technology.
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