Differentiating Digital Twin from Digital Shadow: Elucidating a Paradigm Shift to Expedite a Smart, Sustainable Built Environment

Socio-Technical Analysis of the Benefits and Barriers to Using a Digital Representation of the Global Horse Population in Equine Veterinary Medicine

There is a consensus that future medicine will benefit from a comprehensive analysis of harmonized, interconnected, and interoperable health data. These data can originate from a variety of sources. In particular, data from veterinary diagnostics and the monitoring of health-related life parameters using the Internet of Medical Things are considered here. To foster the usage of collected data in this way, not only do technical aspects need to be addressed but so do organizational ones, and to this end, a socio-technical matrix is first presented that complements the literature. It is used in an exemplary analysis of the system. Such a socio-technical matrix is an interesting tool for analyzing the process of data sharing between actors in the system dependent on their social relations. With the help of such a socio-technical tool and using equine veterinary medicine as an example, the social system of veterinarians and owners as actors is explored in terms of barriers and enablers of an effective digital representation of the global equine population.

Digital Twin Model of Electric Drives Empowered by EKF

Digital twins, a product of new-generation information technology development, allows the physical world to be transformed into a virtual digital space and provide technical support for creating a Metaverse. A key factor in the success of Industry 4.0, the fourth industrial revolution, is the integration of cyber-physical systems into machinery to enable connectivity. The digital twin is a promising solution for addressing the challenges of digitally implementing models and smart manufacturing, as it has been successfully applied for many different infrastructures. Using a digital twin for future electric drive applications can help analyze the interaction and effects between the fast-switching inverter and the electric machine, as well as the system's overall behavior. In this respect, this paper proposes using an Extended Kalman Filter (EKF) digital twin model to accurately estimate the states of a speed sensorless rotor field-oriented controlled induction motor (IM) drive. The accuracy of the state estimation using the EKF depends heavily on the input voltages, which are typically supplied by the inverter. In contrast to previous research that used a low-precision ideal inverter model, this study employs a high-performance EKF observer based on a practical model of the inverter that takes into account the dead-time effects and voltage drops of switching devices. To demonstrate the effectiveness of the EKF digital twinning on the IM drive system, simulations were run using the MATLAB/Simulink software (R2022a), and results are compared with a set of actual data coming from a 4 kW three-phase IM as a physical entity.

Establishing Reliable Research Data Management by Integrating Measurement Devices Utilizing Intelligent Digital Twins

One of the main topics within research activities is the management of research data. Large amounts of data acquired by heterogeneous scientific devices, sensor systems, measuring equipment, and experimental setups have to be processed and ideally be managed by Findable, Accessible, Interoperable, and Reusable (FAIR) data management approaches in order to preserve their intrinsic value to researchers throughout the entire data lifecycle. The symbiosis of heterogeneous measuring devices, FAIR principles, and digital twin technologies is considered to be ideally suited to realize the foundation of reliable, sustainable, and open research data management. This paper contributes a novel architectural approach for gathering and managing research data aligned with the FAIR principles. A reference implementation as well as a subsequent proof of concept is given, leveraging the utilization of digital twins to overcome common data management issues at equipment-intense research institutes. To facilitate implementation, a top-level knowledge graph has been developed to convey metadata from research devices along with the produced data. In addition, a reactive digital twin implementation of a specific measurement device was devised to facilitate reconfigurability and minimized design effort.

The Hitchhiker’s Guide to Fused Twins: A Review of Access to Digital Twins In Situ in Smart Cities

Smart Cities already surround us, and yet they are still incomprehensibly far from directly impacting everyday life. While current Smart Cities are often inaccessible, the experience of everyday citizens may be enhanced with a combination of the emerging technologies Digital Twins (DTs) and Situated Analytics. DTs represent their Physical Twin (PT) in the real world via models, simulations, (remotely) sensed data, context awareness, and interactions. However, interaction requires appropriate interfaces to address the complexity of the city. Ultimately, leveraging the potential of Smart Cities requires going beyond assembling the DT to be comprehensive and accessible. Situated Analytics allows for the anchoring of city information in its spatial context. We advance the concept of embedding the DT into the PT through Situated Analytics to form Fused Twins (FTs). This fusion allows access to data in the location that it is generated in in an embodied context that can make the data more understandable. Prototypes of FTs are rapidly emerging from different domains, but Smart Cities represent the context with the most potential for FTs in the future. This paper reviews DTs, Situated Analytics, and Smart Cities as the foundations of FTs. Regarding DTs, we define five components (physical, data, analytical, virtual, and Connection Environments) that we relate to several cognates (i.e., similar but different terms) from existing literature. Regarding Situated Analytics, we review the effects of user embodiment on cognition and cognitive load. Finally, we classify existing partial examples of FTs from the literature and address their construction from Augmented Reality, Geographic Information Systems, Building/City Information Models, and DTs and provide an overview of future directions.

Digital Twins for Intelligent Green Buildings

At present, the integration of green building, the intelligent building industry and high-quality development are facing a series of new opportunities and challenges. This review aims to analyze the digital development of smart green buildings to make it easier to create contiguous ecological development areas in green ecological cities. It sorts out the main contents of Intelligent Green Buildings (IGB) and summarizes the application and role of Digital Twins (DTs) in intelligent buildings. Firstly, the basic connotations and development direction of IGB are deeply discussed, and the current realization and applications of IGB are analyzed. Then, the advantages of DTs are further investigated in the context of IGB for DT smart cities. Finally, the development trends and challenges of IGB are analyzed. After a review and research, it is found that the realization and application of IGB have been implemented, but the application of DTs remains not quite integrated into the design of IGB. Therefore, a forward-looking design is required when designing the IGBs, such as prioritizing sustainable development, people’s livelihoods and green structures. At the same time, an IGB can only show its significance after the basic process of building the application layer is performed correctly. Therefore, this review contributes to the proper integration of IGB and urban development strategies, which are crucial to encouraging the long-term development of cities, thus providing a theoretical basis and practical experience for promoting the development of smart cities.

Future City, Digital Twinning and the Urban Realm: A Systematic Literature Review

Digitalisation and the future city paradigm are becoming a trend in recent research and practices. Literature discusses digitalisation and its applications as the main gear in the transformation to the ideal future city vision. Yet, the concept of digitalisation is articulated in many interpretations and presented in different applications in the built environment. One emerging application is digital twinning. Literature envisions the potential of digital twinning applications in the urban realm and discusses the cognitive city model and its implications on the future of our cities, its urban realm and the built environment in general. With the evolving themes on the ideal future city model, this systematic review tackles the following questions: what are the key motives and drivers of the future city paradigm; what is a city digital twin; and what are their expected applications. Additionally, how literature envisions the definition of the city users and their experience in the urban realm of the city of the future. This review article explores related literature on the themes of future city model, digital urban realm, digital twinning and city users. The main findings are: identifying key gears of the future city model in literature, exploring city digital twin conceptualization and applications and discussing concepts on the definition of city user and user experience in the city of the future.

Industry 4.0 and Enabling Technologies: Integration Framework and Challenges

Industry 4.0 has revolutionized the traditional production line toward smart factories in a sustainable environment. It represents an intelligent manufacturing network, where machines and products interact without human control. Recently, there have been studies on Industry 4.0 and integrating enabling technologies, and this article aims to summarize a few of the pathbreaking studies and guide future research in this field. This paper reviews 112 journal publications related to Blockchain from 2017 to 2021 in the Science Citation Index (SCI) and Social Science Citation Index (SSCI) database. The selected papers are grouped into ten categories. This overview indicates that research of integrating Industry 4.0 enabling technologies needs to focus on developing better privacy and security mechanisms and a design framework for integrating the 5G network system into the platform.

Computational Intelligence in Security of Digital Twins Big Graphic Data in Cyber-Physical Systems of Smart Cities

This investigation focuses on the application of computational intelligence to the security of Digital Twins (DTs) graphic data of the Cyber-Physical System (CPS). The intricate and diverse physical space of CPS in the smart city is mapped in virtual space to construct the DTs CPS in the smart city. Besides, Differential Privacy Frequent Subgraph-Big Multigraph (DPFS-BM) is employed to ensure data privacy security. Moreover, the analysis and prediction model for the DTs big graphic data (BGD) in the CPS is built based on Differential Privacy-AlexNet (DP-AlexNet). Alexnet successfully solves the gradient dispersion problem of the Sigmoid function of deep network structures. Finally, the comparative analysis approach is utilized to verify the performance of the model reported here by comparing it with Long Short-Term Memory, Convolutional Neural Network, Recurrent Neural Network, original AlexNet, and Multi-Layer Perceptron in a simulation experiment. Through the comparison in the root mean square error, the mean absolute error, the mean absolute percentage error, training time, and test time, the model proposed here outperforms other models regarding errors, time delay, and time consumption. In the same environment, the system performs better with multi-hop paths, extra relays, and a high fading index; in that case, the outage probability is minimal. Therefore, the DP-AlexNet model is suitable for processing BGD. Moreover, its speed acceleration is more apparent than that of other models, with a higher SpeedUp indicator. The research effectively combines data mining and data security, which is of significant value for optimizing the privacy protection technology of frequent subgraph mining on a single multi-graph. Besides, the constructed DTs of CPS can provide excellent accuracy and a prominent acceleration effect on the premise of low errors. In addition, the model reported here can provide reference for the intelligent and digital development of smart cities.

Sustainability in the Civil Construction Sector Supported by Industry 4.0 Technologies: Challenges and Opportunities

The civil construction sector is under pressure to make construction processes more sustainable, that is, aligned with economic, social, and environmental sustainability. Thus, the research question considers: How do Industry 4.0 Technologies help civil construction face challenges and identify new opportunities to become sustainable? The general objective of this work is to offer a current overview of publications that associate the civil construction sector; Industry 4.0 Technologies and sustainability, and identify the challenges and opportunities of the Industry 4.0 Technologies set to contribute to sustainability achievement. The research method was a bibliographic review combined with bibliometric analysis in SCOPUS databases. The results show that civil construction faces the challenge of reducing the consumption of natural resources, ensuring safe work, and optimizing processes, especially handwork. However, the insertion of Industry 4.0 Technologies into civil construction has allowed sensors, robots, modelling and simulation systems, artificial intelligence, and drones to have their productivity, efficiency, safety, strategic and environmental management enhanced. Furthermore, Industry 4.0 Technologies can contribute to civil construction through innovative, sustainable, and technological solutions focused on the flow of work, which can provide growth through the balance of costs/benefits in the management of projects and works. Thus, it is expected that this article will contribute to discussions around the possibility of construction becoming sustainable with the support of Industry 4.0 Technologies.

Digital Twins-Based Impact Response Prediction of Prestressed Steel Structure

Civil infrastructure O&M requires intelligent monitoring techniques and control methods to ensure safety. Unfortunately, tedious modeling efforts and the rigorous computing requirements of large-scale civil infrastructure have hindered the development of structural research. This study proposes a method for impact response prediction of prestressed steel structures driven by digital twins (DTs) and machine learning (ML). The high-fidelity DTs of a prestressed steel structure were constructed from the perspective of both a physical entity and virtual entity. A prediction of the impact response of prestressed steel structure’s key parts was established based on ML, and a structure response prediction of the parts driven by data was realized. To validate the effectiveness of the proposed prediction method, the authors carried out a case study in an experiment of a prestressed steel structure. This study provides a reference for fusion applications with DTs and ML in impact response prediction and analysis of prestressed steel structures.

Digital Twin for Training Bayesian Networks for Fault Diagnostics of Manufacturing Systems

Smart manufacturing systems are being advocated to leverage technological advances that enable them to be more resilient to faults through rapid diagnosis for performance assurance. In this paper, we propose a co-simulation approach for engineering digital twins (DTs) that are used to train Bayesian Networks (BNs) for fault diagnostics at equipment and factory levels. Specifically, the co-simulation model is engineered by using cyber–physical system (CPS) consisting of networked sensors, high-fidelity simulation model of each equipment, and a detailed discrete-event simulation (DES) model of the factory. The proposed DT approach enables injection of faults in the virtual system, thereby alleviating the need for expensive factory-floor experimentation. It should be emphasized that this approach of injecting faults eliminates the need for obtaining balanced data that include faulty and normal factory operations. We propose a Structural Intervention Algorithm (SIA) in this paper to first detect all possible directed edges and then distinguish between a parent and an ancestor node of the BN. We engineered a DT research test-bed in our laboratory consisting of four industrial robots configured into an assembly cell where each robot has an industrial Internet-of-Things sensor that can monitor vibrations in two-axes. A detailed equipment-level simulator of these robots was integrated with a detailed DES model of the robotic assembly cell. The resulting DT was used to carry out interventions to learn a BN model structure for fault diagnostics. Laboratory experiments validated the efficacy of the proposed approach by accurately learning the BN structure, and in the experiments, the accuracy obtained by the proposed approach (measured using Structural Hamming Distance) was found to be significantly better than traditional methods. Furthermore, the BN structure learned was found to be robust to variations in parameters, such as mean time to failure (MTTF).

Digital Twin and Smart Manufacturing in Industries: A Bibliometric Analysis with a Focus on Industry 4.0

Technology is being used in our society in all areas, mostly in industry, and generates the most interest in current research since it is a part of day-to-day activities. The main objective of this research was to use bibliometric analysis to analyze the production of scientific literature on digital twin and smart manufacturing with a focus on Industry 4.0, using information from the Web of Science database. To conduct the study, the keywords necessary for data selection were chosen, and then analyzed based on different variables such as author productivity, citations, most productive institutions, publishers with the highest number of publications, scientific document classification, countries with the highest number of publications, and a network analysis using VOSviewer. The results showed Tao F. and Soderberg R. were the main authors, that China was the country with the highest knowledge, and Elsevier was the main publisher. Although the subject has only been in publication for five years, digital twin will constitute an important part of future technologies due to its rapid ascension, proof of this being its yearly productivity (2020 producing the highest number of materials). Papers published in 2021 were excluded, but the difference between the numbers of materials found and those analyzed shows that 2021 will be even more productive than 2020.

An Innovative Infrastructure Based on Shape-Adaptive RIS for Smart Industrial IoTs

Industrial Internet of Things (IoTs) applications will have higher demands in terms of system capacity and wireless transmission performance in the future. Reconfigurable intelligent surfaces (RIS) for the wireless transmission environment reconstruction is currently a research hotspot in the field of wireless communication. In this paper, RIS is applied to industrial IoTs scenarios, and an innovative infrastructure based on shape-adaptive RIS suitable for smart industrial IoTs is proposed. Based on the many advantages of RIS, the proposed infrastructure can meet the significant demands of industrial IoTs in terms of network coverage, system capacity, transmission rate, and communication security. Further, in order to achieve flexible RIS phase adjustment in wider ranges, in this paper, a shape-adaptive RIS mechanism is proposed to regulate RIS-reflected waves in the shape domain, which can be achieved by scalable RIS unit sizes and macroscopically changeable physical shapes. This mechanism can reduce the requirements for RIS-controller and artificial-unit design and expand the spatial distribution of reflected beams, whereas this is difficult to achieve by conventional regulations in the spatial, time, and frequency domains. The simulation results show that, in terms of system-coverage probability, the system performance of RIS is better than that of general communication systems, and the performance of the deformable RIS-based system is the best. In terms of channel gain, the performance of the deformable RIS is related to its surface size. Moreover, the path loss caused by the long-distance transmission of wireless signals can be compensated for by increasing the RIS surface area.

Drivers for Digital Twin Adoption in the Construction Industry: A Systematic Literature Review

Digital twin (DT) is gaining increasing attention due to its ability to present digital replicas of existing assets, processes and systems. DT can integrate artificial intelligence, machine learning, and data analytics to create real-time simulation models. These models learn and update from multiple data sources to predict their physical counterparts’ current and future conditions. This has promoted its relevance in various industries, including the construction industry (CI). However, recognising the existence of a distinct set of factors driving its adoption has not been established. Therefore, this study aims to identify the drivers and integrate them into a classification framework to enhance its understanding. Utilising popular databases, including Scopus, Web of Science, and ScienceDirect, a systematic literature review of 58 relevant DT adoptions in the CI research was conducted. From the review, the drivers for DT adoption in the CI were identified and classified. The results show that developed countries such as the UK, US, Australia, and Italy have been the top countries in advancing DT adoption in the CI, while developing countries have made commendable contributions. A conceptual framework has been developed to enhance the successful adoption of DT in the CI based on 50 identified drivers. The major categories of the framework include concept-oriented drivers, production-driven drivers, operational success drivers, and preservation-driven drivers. The developed framework serves as a guide to propel DT adoption in the CI. Furthermore, this study contributes to the body of knowledge about DT adoption drivers, which is essential for DT promotion in the CI.

Construction Theory for a Building Intelligent Operation and Maintenance System Based on Digital Twins and Machine Learning

The operation and maintenance (O&M) of buildings plays an important role in ensuring that the buildings work normally, as well as reducing the damage caused by functional errors. There are obvious problems in the traditional O&M modality, and an effective way to solve them is to make the model smarter. In this paper, a digital twin framework for building operation is proposed, which consists of two key components: a digital twin O&M model and a machine learning algorithm. The process of establishing the digital twin model is introduced in detail, and the method is explained according to the structure, equipment, and energy consumption characteristics of the model. A mechanism of fusing the digital twin and machine learning algorithm is proposed and the prediction process based on an artificial neural network (ANN) is shown. Finally, based on a systematic summary of the modeling process and fusion mechanism, the development path and overall structure of the intelligent O&M system utilizing digital twins is proposed.

Getting Real: The Challenge of Building and Validating a Large-Scale Digital Twin of Barcelona’s Traffic with Empirical Data

Large-scale microsimulations are increasingly resourceful tools for analysing in detail citywide effects and alternative scenarios of our policy decisions, approximating the ideal of ‘urban digital twins’. Yet, these models are costly and impractical, and there are surprisingly few published examples robustly validated with empirical data. This paper, therefore, presents a new large-scale agent-based traffic microsimulation for the Barcelona urban area using SUMO to show the possibilities and challenges of building these scenarios based on novel fine-grained empirical big data. It combines novel mobility data from real cell phone records with conventional surveys to calibrate the model comparing two different dynamic assignment methods for getting an operationally realistic and efficient simulation. Including through traffic and the use of a stochastic adaptive routing approach results in a larger 24-hour model closer to reality. Based on an extensive multi-scalar evaluation including traffic counts, hourly distribution of trips, and macroscopic metrics, this model expands and outperforms previous large-scale scenarios, which provides new operational opportunities in city co-creation and policy. The novelty of this work relies on the effective modelling approach using newly available data and the realistic robust evaluation. This allows the identification of the fundamental challenges of simulation to accurately capture real-world dynamical systems and to their predictive power at a large scale, even when fed by big data, as envisioned by the digital twin concept applied to smart cities.

A Conceptual Framework for Estimating Building Embodied Carbon Based on Digital Twin Technology and Life Cycle Assessment

Low-carbon building design requests an estimation of total embodied carbon as the environmental performance metric for comparison of different design options in early design stages. Due to a lack of consensus on the system boundaries in building life cycle assessment (LCA), the carbon estimation results obtained by the current methods are often disputable. In this regard, this paper proposes a method for estimating building embodied carbon based on digital twin technology and LCA. The proposed method is advantageous over others by providing (1) a cradle-to-cradle LCA and (2) an automated data communication between LCA and building information modelling (BIM) databases. Because data for the processes in the life cycle are collected via digital twin technology in a standard and consistent way, the obtained results will be considered credible. So far, a conceptual framework is developed based on a comprehensive literature review, which consists of three parts. In the first part, formulas for LCA are given. In the second part, a hybrid approach combining semantic web with a relational database for BIM and radio-frequency identification (RFID) integration is described. In the third part, how to design the LCA database and how to link LCA with BIM are described. The conceptual framework proposed is tested for its reasonableness by a small hypothetical case study.

Digital Twin-Driven Human Robot Collaboration Using a Digital Human

Advances are being made in applying digital twin (DT) and human–robot collaboration (HRC) to industrial fields for safe, effective, and flexible manufacturing. Using a DT for human modeling and simulation enables ergonomic assessment during working. In this study, a DT-driven HRC system was developed that measures the motions of a worker and simulates the working progress and physical load based on digital human (DH) technology. The proposed system contains virtual robot, DH, and production management modules that are integrated seamlessly via wireless communication. The virtual robot module contains the robot operating system and enables real-time control of the robot based on simulations in a virtual environment. The DH module measures and simulates the worker’s motion, behavior, and physical load. The production management module performs dynamic scheduling based on the predicted working progress under ergonomic constraints. The proposed system was applied to a parts-picking scenario, and its effectiveness was evaluated in terms of work monitoring, progress prediction, dynamic scheduling, and ergonomic assessment. This study demonstrates a proof-of-concept for introducing DH technology into DT-driven HRC for human-centered production systems.

A Digital Twin-Based Platform towards Intelligent Automation with Virtual Counterparts of Flight and Air Traffic Control Operations

Automation technologies have been deployed widely to boost the efficiency of production and operations, to trim the complicated process, and to reduce the human error involved. Nevertheless, aviation remains human-centred and requires collaboration between different parties. Given the lack of a collaborative decision-making training platform for air traffic operations in the industry, this study utilises the concept of cyber-physical systems (CPS) to formulate a system architecture for pilots and air traffic control officers training in collaborative decision making by linking and integrating the virtual counterparts of flights and air traffic control operations. Collaborative decision-making training and the corresponding intelligent automation aids could be realised and supported. A performance analysis via a flight task undertaken with different computational load settings was prepared to evaluate the platform’s latency and integrity. The latency is presented using its 95% confidence interval, and integrity is presented using the percentage of data loss during wireless transmission. The results demonstrated convincing performance and a promising system robustness in both domains.

Designing Construction 4.0 Activities for AEC Classrooms

This article describes the outcomes of the development of the project MATES to STEAM. The project is aimed at integrating Construction 4.0 content to a recently started new degree on Technologies on Civil Engineering. This integration is underpinned by the creation of STEAM-rich activities that can complement such degree. The philosophical design of these activities followed three requirements: (i) the activities should infuse Construction 4.0-related technologies, (ii) the activities should foster motivation among students with a STEAM vision by-design and (iii) the activities should be designed with a hardware-software independent perspective (open-source, accessible, affordable). Cornerstone and capstone projects as well as a set of workshops represent the demonstrators of these activities. All these demonstrators are knitted together in a single path in which an educational attempt to fill the identified Construction 4.0 gaps is proposed. The STEAM perspective provides completeness to the whole development. During the last two years, the project was developed and the design, the development and implementation of several demonstrators were completed. In the years to come, a systematic deployment and analysis of such demonstrators is expected when a full implementation of the new degree of Technologies in Civil Engineering will be addressed.

Digital Twins Supporting Efficient Digital Industrial Transformation

Industry 4.0 applications help digital industrial transformation to be achieved through smart, data-driven solutions that improve production efficiency, product consistency, preventive maintenance, and the logistics of industrial applications and related supply chains. To enable and accelerate digital industrial transformation, it is vital to support cost-efficient Industry 4.0 application development. However, the development of such Industry 4.0 applications is currently expensive due to the limitations of existing IoT platforms in representing complex industrial machines, the support of only production line-based application testing, and the lack of cost models for application cost/benefit analysis. In this paper, we propose the use of Cyber Twins (CTs), an extension of Digital Twins, to support cost-efficient Industry 4.0 application development. CTs provide semantic descriptions of the machines they represent and incorporate machine simulators that enable application testing without any production line risk and cost. This paper focuses on CT-based Industry 4.0 application development and the related cost models. Via a case study of a CT-based Industry 4.0 application from the dairy industry, the paper shows that CT-based Industry 4.0 applications can be developed with approximately 60% of the cost of IoT platform-based application development.

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human-robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

Digital Twin for FANUC Robots: Industrial Robot Programming and Simulation Using Virtual Reality

A Digital Twin is the concept of creating a digital replica of physical models (such as a robot). This is similar to establishing a simulation using a robot operating system (ROS) or other industrial-owned platforms to simulate robot operations and sending the details to the robot controller. In this paper, we propose a Digital Twin model that assists in the online/remote programming of a robotic cell by creating a 3D digital environment of a real-world configuration. Our Digital Twin model consists of two components, (1) a physical model: FANUC robot (M-10iA/12), and (2) a digital model: Unity (a gaming platform) that comes with specialized plugins for virtual and augmented reality devices. One of the main challenges in the existing approach of robot programming is writing and modifying code for a robot trajectory that is eased in our framework using a Digital Twin. Using a Digital Twin setup along with Virtual Reality, we observe the trajectory replication between digital and physical robots. The simulation analysis provided a latency of approximately 40 ms with an error range of −0.28 to 0.28∘ across the robot joint movements in a simulation environment and −0.3 to 0.3∘ across the actual robot joint movements. Therefore, we can conclude that our developed model is suitable for industrial applications.

An Approach for Realizing Hybrid Digital Twins Using Asset Administration Shells and Apache StreamPipes

Digital twins (DTs) are digital representations of assets, capturing their attributes and behavior. They are one of the cornerstones of Industry 4.0. Current DT standards are still under development, and so far, they typically allow for representing DTs only by attributes. Yet, knowledge about the behavior of assets is essential to properly control and interact with them, especially in the context of industrial production. This behavior is typically represented by multiple different models, making integration and orchestration within a DT difficult to manage. In this paper, we propose a new approach for hybrid DTs by intertwining different DT models. We also show how to realize this approach by combining the Fraunhofer Asset Administration Shell (AAS) Tools for Digital Twins (FAST) to create Industry 4.0-compliant DTs with Apache StreamPipes to implement and manage multiple DT models. Our prototype implementation is limited to a subset of the AAS metamodel and pull-based communication between FAST and an external Apache StreamPipes instance. Future work should provide full support for the AAS metamodel, publish/subscribe-based communication, and other execution environments as well as deployment strategies. We also present how this approach has been applied to a real-world use case in the steel production industry.