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A Medical Cyber-Physical System (MCPS) represents a sophisticated healthcare framework seamlessly integrating cyber and physical elements to enhance medical processes, diagnostics, and patients. The integration of Artificial Intelligence (AI) into the healthcare system has been pivotal in advancing intelligent MCPS and ushering in an era of advanced healthcare solutions. The paradigm of smart hospitals aspires to implement intelligent solutions seamlessly integrating hardware and software to control, supervise, and monitor patients while assisting healthcare professionals. Such solution is essential for smart decision-making and enhancing healthcare services. However, complete utilization of this intelligent MCPS relies on an effective framework that should facilitate the interaction among patients, medical devices, AI services and hospital staff. This paper introduces a Digital Twin (DT)-based Smart Medical Cyber-Physical System (DT-MCPS) designed to enhance smart hospitals. Leveraging DT technology, DT-MCPS constructs a virtual replica of the hospital, facilitating precise control and supervision of patient care, coupled with service optimization through comprehensive data integration. DT-MCPS promotes personalized decision-making by seamlessly integrating medical records and real-time monitoring of physiological data, enabling predictive insights into disease progression. Moreover, DT-MCPS employs a model-based platform founded on web services to monitor the patient’s state in real-time while accurately simulating the hospital medical systems workflows and contributing to long-term health management. Experimental results showcase the efficacy of DT-MCPS in enhancing hospitalization services, streamlining real-time control, and achieving highly precise personalized patient diagnostics.

For realizing smart manufacturing, recent studies have been focusing on the capabilities of digital twins (DTs) that offer a virtual portrayal of a process or system and thereby facilitate real-time monitoring, analysis, and optimization. DTs construct a “digital model” of factories by using advanced technologies such as smart sensors, Internet of Things (IoT), artificial intelligence (AI) techniques, and optimization processes for executing critical decisions for enhancing productivity and predicting future events. Therefore, simulation-based optimization techniques play key supporting roles in the decision-making process of the DT shop. Thus, to enhance competitiveness in industrial companies, discourse on properly designing simulation-based optimization techniques begets significant study. This study aims to address a simulation-based optimization problem with stochastic constraint (SO${}_{\mathrm{\text{SC}}})$ using a hybrid particle swarm optimization (PSO). Given the large solution space, the PSO supports design space exploration. However, since all solutions must be verified for feasibility, the optimal computing allocation strategy (OCAS) strategy for SO${}_{\mathrm{\text{SC}}}$ is proposed to allocate the computing budget efficiently. OCAS is an improved version of optimal computing budget allocation for constrained optimization (OCBA-CO), specifically designed for complex systems with an expansive design space that possesses multiple local optimal solutions. During a typical PSO algorithm searching process, OCBA-CO is applied separately for each generation, resulting in a significant number of replications merely distinguishing similar solutions. To overcome this problem, our study proposes the PSO${}_{\mathrm{\text{OCAS}}}$, which combines PSO with OCAS to avoid wasting simulation runs on similar solutions. Two quantitative experiments are performed to assess the efficacy of PSO${}_{\mathrm{\text{OCAS}}}$ compared to other competitors. The simulation results indicate that the PSO${}_{\mathrm{\text{OCAS}}}$ algorithm presents a competitive and superior solution in terms of both quality and searching efficiency.

Changing requirements cause flexible automated Production Systems (aPS) to evolve over decades. Digital Twins (DT) of the different hierarchy levels and design steps ease this evolution, e.g., by enabling requirement analysis and compatibility checks ahead of any physical changes. To ensure up-to-date models and integrate additional knowledge, information gained during operation is included in DTs. Consequently, evolvability, decomposability, control software modularity, and learning during operation are identified as four requirements to achieve such evolvable DTs. Concepts to realize every requirement are introduced and exemplarily validated using a demonstrator machine. AutomationML (AML), the XML-based vendor neutral language for information modeling and exchange in between different disciplines and their tools and product classification systems like ECLASS that specify components attributes vendor neutral enable evolvability during the design phase. Decomposability is achieved by assembling DTs of components according to ISA 88 levels from control unit to facility. A control primitive concept that realizes control software modularity is introduced and validated. Based on data analytics and operation data the DT can be updated by using the versioning mechanism of AML. Thereby, the DT for the next machine generation is improved with knowledge from operation and represents the already existing machine more precisely.

In the era of Industry 4.0, 3D printing has shown significant outcomes. To address the challenges of large-format complex material printing and forming, such as spatial constraints and excessive support structures in traditional 3D printing, the integration of industrial robots with 3D printing technology is proposed. However, robotic 3D printing introduces challenges in path planning and real-time optimization. This paper presents a methodology for path planning and real-time optimization of robotic arms on a 3D printing platform. The approach involves adjusting the printing path by modifying the nozzle printing posture and implementing obstacle avoidance algorithms. The study uses geometric and algebraic methods to optimize the robotic arm trajectory to improve the precision of reaching print points, reduce the printing cycle, and minimize material wastage. To verify the feasibility of this method, a case study in 3D printing is conducted to examine the practical application of motion planning for robotic arms based on digital twin technology.

This study aims to deal with a dynamic scheduling problem of a real Flow Shop follow-up of an assembly process considering the specific constraints and requirements of a brass accessories manufacturing (BAM) company. We basically set up a Digital Twin-driven dynamic scheduling approach for Industry 4.0 by addressing uncertainties of machine availability. In fact, the setup of this Digital Twin (DT) is the outcome of the combination of both optimization and simulation. For the first step, we have elaborated a Mixed Integer Linear Programming (MILP) scheduling model by taking into account the dedicated requisites of our case study. Concerning simulation, a 3D simulation platform of a workshop producing brass accessories controlled by a Cyber-Physical Production System (CPPS) has been developed, in our previous work, including constraints and stochastic aspects. The simulation constraints are difficult or impossible to be modeled in the MILP model. These designs are integrated with the real workshop to construct the Digital Twin. The proposed tool enables the rescheduling of production orders based on machine disturbances and unpredictability. Validation scenarios have been designed and conducted to highlight the efficacy of the Digital Twin approach utilizing the brass accessories case study. We are confident that this represents the initial endeavor addressing the optimization of production rescheduling in a flow shop follow-up of a mixed assembly system.

Virtual reality (VR) has advanced as a collaborative, realistic, and creative computation technique in recent decades. With organizations becoming digitally more focused and employees’ experience changed by technology, manager’s face and continue to confront several obstacles in the digital transformation process. Recent advances in information integration have been made possible by implementing the improved digital twin (DT) paradigm and its use in the workspace. To solve these problems, simulated convergence, realistic dynamic computational decision-making, and other tools are effective. This helps to complete activities with physical models and records. Thereby, this paper presents a Visually Improved Digital Media Communication Framework (VIDMCF) using VR technology and DT. Incorporating all information, displaying the whole procedure, avoiding challenges, closing loops, optimizing repetitive processes, and making complex decisions in real-time can be aided by reproducing physical systems in the virtual design and adding VR and digital mirror twin to the output of digital media. The proposed model can achieve connectivity and convergence among the realistic atmosphere and the digital environment’s virtual system in cyber-real-space harmony over the life cycle.

Digital twin aims to create a virtual model for a physical structure by combining measurement data in structural health monitoring. The most important feature is to achieve the physical structure-monitoring data synchronization. For this purpose, a physics-data hybrid framework to develop the bridge digital twin model in structural health monitoring is proposed in the paper. The physical base is firstly formed by the finite element model of the digital representation for the physical bridge that can fully incorporate both structural geometry and structural state. The data base is then built by all measurement data of the monitored bridge. By defining the context that is common to both physical base and data base, the mirror relationship between physical base and data base for the specified context is formulated. To achieve the best matching of the mirror relationship by minimizing process, the digital twin model in terms of the specified context can be developed. In such a way, the proposed framework integrates physical knowledge and data intelligence into one model. A demonstration of a simulated simply supported beam is provided to show how the digital twin model is developed by using proposed physics-data hybrid framework. It is found that the generated digital twin model is consistent with the current structural state of the beam. The presented physics-data hybrid framework helps in clearer understanding of the realization of digital twin model in structural health monitoring, providing a new perspective for smart bridge solutions.

The continuous propagation of Artificial Intelligence (AI) techniques has led to engineering of so-called “humanoid” socio-technical settings, which include mimicking cognitive and social skills. With the advent of cyber-physical systems Digital Twins have become baseline representations of systems integrating cognitive and social behaviour models. When AI components become part of those representations capturing social relations and reflecting on behaviour, they are termed Digital Selves, and require specific development techniques. In this paper, we introduce a Knowledge Management approach to investigate the emergence of socially reflective behaviour like awareness and consciousness in artificial agents. We identify and explain mandatory properties of Digital Selves relevant for modelling digital societies that need to be studied and become part of a transparent and explainable knowledge base for trans-human developments.

This study presents a hybrid physical–virtual digital twin system to enhance additive manufacturing (AM). The system aims to achieve real-time monitoring, predictive modeling, and improved 3D printing control. It integrates sensing-driven and simulation-driven digital twins into a comprehensive hybrid digital twin environment (HDTE) tailored for the Creality Ender-5 Plus 3D printer within the Unity 3D virtual environment. The AM process simulation employs a three-dimensional transient finite element model, enabling precise predictions of thermodynamics, residual stress, final distortion, and solidification parameters. To enhance real-time insights, external sensors like thermocouples, thermal cameras, and LIDAR ranging sensors continuously collect temperature and location data during 3D printing. Experimental results confirm the HDTE’s accuracy. Position assessment experiments show their ability to replicate component positions under varying conditions, while temperature assessment experiments demonstrate their capacity to mimic temperature variations promptly and accurately. The study also provides a detailed analysis of nodal temperature distribution within a 3D printed plate, indicating potential for predictive defect detection and print parameter optimization. Additionally, the study integrates a closed-loop control mechanism, ensuring rigorous quality control, although a detailed exposition of this system is reserved for future publications. This paper bridges the gap between virtual design and physical AM production, offering real-time monitoring, predictive capabilities, and automated control. These advancements enhance efficiency, reduce material waste, and promote sustainability. As this system evolves, it promises to revolutionize additive manufacturing, ushering in a data-driven era of intelligent 3D printing across industries.

Systems engineering practices are evolving to address fast-changing needs in fielding complex systems. These needs create an environment in which system needs evolve or change too quickly to be tracked or managed by humans’ natural capabilities. We propose that systems engineering must aid systems engineering managers by providing architectural alternatives and design options. Further, as systems become more complex and dynamic, there is an increased need to identify hidden risks, model emergent behavior, and expose hidden patterns in the behavior of stakeholders. Systems engineering needs to evolve to build fast-fielded, resilient, and adaptive systems that leverage positive reinforcement feedback loops with multiple experimental and real-world information sources. The very basis of systems engineering must evolve from today’s development paradigms to a future that leverages modeling, simulation, and artificial intelligence to drastically improve the capability and agility for developing new systems. This paper proposes a common way forward to enable this new form of complex adaptive systems engineering.

Accurate forecast of spare parts demand is of great significance for modern enterprises to provide accurate support and improve market competitiveness. In most studies, mathematical laws are used to forecast, without enough consideration of the actual operation of equipment and the fact that the accuracy of spare parts demand forecasting is not high, which cannot adapt to the new characteristics of complex equipment use environment and fierce market competition in modern enterprises. The digital twin model can be used to forecast the demand for spare parts more accurately and guide modern enterprises to carry out accurate support. By analyzing the current spare parts demand of modern enterprises, the paper puts forward the forecasting ideas of spare parts demand based on the digital twin model by using the digital twin model of equipment maintenance management in modern enterprises. In the digital twin model, the theoretical demand forecasting model of spare parts based on life distribution of replaceable units is introduced, and the sensitivity coefficient system of spare parts demand of replaceable units to operation and environment is constructed. The digital twin model is used to feedback train the sensitivity coefficient to obtain the reliable spare parts demand rules. Based on the theoretical demand and sensitivity coefficient of spare parts, the forecasting method of spare parts demand is given, and the spare parts demand forecasting model is established. Through case analysis, the feasibility and accuracy of the forecasting model are verified.

During the past years, intelligent manufacturing has attracted enormous attention from both academia and industry. Smart factories/workshops/production lines are important carriers for realizing intelligent manufacturing. However, there are difficulties in smart factories in processing multi-source heterogeneous data, in integrating virtual and physical worlds, and in achieving continuous and iterative optimization with real-time feedback. Digital twin, which is able to deeply integrate physical and virtual worlds, provides an important technology for easing the difficulties mentioned above. The construction of a digital twin system can solve the problem of data fusion, analysis, processing, and applications in smart factory. Digital production lines are the foothold of a smart factory. Building a digital twin production line can facilitate rapid practice of digital twins and promote the development of intelligent manufacturing. To this end, this paper proposes a development framework for a digital twin production line with closed-loop control based on a mechatronics approach. The detailed development process is given based on the framework. The framework proposed may provide some inspiration and insights for design and development of digital twin production lines.

Facing the challenges of production management of manufacturing enterprises and the demand for intelligent control of manufacturing system abnormalities, this paper proposes a production abnormal event diagnosis method based on digital twin technology to achieve intelligent tracing of the causes of production abnormal events and improve the problems of poor timeliness and lack of feedback mechanism for the diagnosis of production abnormal events of complex products. First, a complex product production abnormal event diagnosis model is constructed, in which physical production workshop, virtual production workshop, workshop twin data platform and abnormal event diagnosis service system work together. Second, the joint method of Bayesian network (BN) and C5.0 decision tree algorithm (C5.0) is used to achieve the diagnosis of production abnormal events. Finally, the feasibility and effectiveness of the scheme are verified by the arithmetic example of key stations in the bogie production workshop. The diagnostic accuracy of 92.49% of the BN–C5.0 model is higher than that of the conventional C5.0 decision tree model with 79.94% accuracy. The proposed method and mechanism provide a reference model for the application of digital twin to the diagnosis of abnormal production events.

To reduce the complexity of monitoring and management of robots in service, a six-axis robot control system based on digital twin is proposed. Based on 3D printing technology, a six-axis robot is developed. At the same time, the kinematics of the robot is analyzed, and its kinematics model is built using the D-H rule. The forward and reverse kinematics of the robot are solved. Through the two-way data interaction between the model layer and the entity layer, the simulation operation of the robot and the twin synchronous operation of the virtual real robot are realized. Based on the real-time data drive, the key parameters of the robot are monitored, and the health parameter table of the current, voltage, joint vibration, and other parameters of the robot system is established. Based on the idea of comparison of the same kind, the abnormal state detection of the robot is realized through quantitative analysis. Finally, the feasibility of the proposed system is verified by experiments.

As an enabling technology and means to practice the concept of intelligent manufacturing, digital twin has attracted extensive attention from both industry and academia. In view of the lack of concrete implementation methods of digital twin robot assembly lines, and the complex implementation process, and long development cycle using existing methods, the implementation of digital twin robot assembly lines is a significant research problem. In view of this, this paper proposes an architecture as well as an implementation method of a digital twin system for a robot assembly line. Taking a robot assembly production line as an example, a digital twin architecture and modular development approach are proposed, and three key technologies for implementing the digital twin system, namely virtual model construction, real-time data acquisition and accurate virtual-real mapping, are elaborated. The architecture and implementation method can quickly and effectively facilitate building a digital twin system of the robot assembly line, and realizing real-time monitoring and intelligent analysis of all elements of the physical robot production line.

With the rapid advancement of metaverse technology, the military field has gradually begun to pay attention to the potential applications of metaverse technology. The military metaverse is a new and comprehensive military ecosystem that integrates the virtual world and the real world, unlocking new possibilities in areas such as military training, education, exercises, combat support, and equipment research and development. This paper analyzes the concept and connotation of the military metaverse and presents its main features and key technologies, as well as the role it plays in the military field. Furthermore, it proposes the primary application scenarios of the military metaverse and provides an application architecture. Finally, the paper offers a forward-looking perspective on the future development of military metaverse.

In order to solve the selection problems such as multiple products, complex scenarios, and difficulty in supporting complex solutions, the product selection push model for complex solutions was proposed, the selection cloud platform was set up, and the platform architecture, composition, and operating mechanism were given. Based on the digital twin five-dimensional framework, the modeling process of the product selection cloud platform was detailed, and in the demand stage, the function model of the model was determined by the collaborative filter algorithm, which gave a comprehensive description of its functions. Through the construction of multi-platform fusion and algorithm model, the active push, scheme automatic process, and verification function of solution product selection have been realized. The solution has been applied in the selection of the smart security community scheme, verifying the effectiveness and practicality of the platform.

The traditional fault diagnosis approach for cranes often results in unclear control status, protracted fault location times, prolonged equipment downtime, and other related challenges. To address these issues, this paper proposes embedding the stacked denoising auto-encoder, multi-class support vector machine, and fuzzy Bayesian network (SDAE-MCSVM-FBN (SMF)) hidden cascade fault diagnosis method into a digital twin model of the crane. The approach utilizes the digital twin to monitor the crane’s state and identify virtual environment faults to make decisions to reduce the direct operation of complex equipment and enhance operational efficiency. This paper first constructs a digital twin model of the crane to describe its composition and interaction behavior within the system. Next, it solves the hidden cascade faults in cranes using the SMF method. Finally, this approach develops a prototype system for a digital twin of cranes with an example in a large steel mill used for verification and analysis purposes. Experimental results demonstrate that compared with traditional manual inspection methods, this paper’s proposed hidden cascade fault method reduces repair time by 24.5%–32.8% while decreasing downtime by 20.5%–32.4%. The outcome highlights the effectiveness of our diagnostic approach and demonstrates digital twin technology can minimize frequent maintenance requirements by personnel while protecting crane service life overall.

In the realm of advanced manufacturing, the integration of digital technologies has revolutionized industrial processes; this paper explores the fusion of nature-inspired design principles with advanced robotics in the context of a Cartesian pneumatically controlled robotic system. Leveraging the elegance of biomimicry, the system integrates a claw-inspired gripper for precision pick-and-place operations. The study employs digital twin technology to enhance the understanding and optimization of the robotic system. By embracing nature-driven design, the Cartesian robotic arm is engineered for enhanced efficiency and adaptability. The biomimetic approach not only improves performance but also aligns with sustainability goals. The abstract encapsulates the essence of harmonizing Cartesian systems, pneumatic control, claw-inspired gripper, digital twins, pick-and-place operations, and nature-driven design to advance the forefront of robotics and automation. Furthermore, this paper addresses the aspect of human–robot collaboration by considering safety protocols and collision avoidance mechanisms when the robot operates in proximity to human workers. The digital twin’s potential extends beyond replication and optimization, paving the way for safer and more efficient manufacturing processes. The report details the entire development process, from the initial understanding of the physical system to creating the digital twin. This work signifies a valuable contribution to manufacturing, robotics, and digital simulation, offering a versatile tool for optimizing industrial processes and enhancing the efficiency of Cartesian robot-assisted plastic injection moulding operations.

The coronavirus pandemic has placed renewed focus on expanded access (EA) programs to provide compassionate use exceptions to the waves of patients seeking medical care in treating the novel disease. While commendable, justifiable, and compassionate, EA programs are not designed to collect the necessary vital clinical data that can be later used in the New Drug Application process before the U.S. Food and Drug Administration (FDA). In particular, they lack the necessary rigor of properly crafted and controlled randomized controlled trials (RCT) which ensure that each patient closely monitored for side effects and other potential dangers associated with the drug, that the data is documented, stable and are traceable and that the patient population is well defined with the defined target condition. Overall, while RCTs is deemed to be of the most reliable methodologies within evidence-based medicine, morally, however, they are problematic in EA programs. Nevertheless, actionable data ought to be collected from EA patients. To this end, we look to the growing incorporation of real-world data real-world evidence as increasingly useful substitutes for data collected via RCTs, including the ethical, legal and social implications thereof. Finally, we suggest the use of digital twins as an additional method to derive causal inferences from real-world trials involving expanded access patients.