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Offsite construction (OSC) techniques are argued to provide superior quality and shorter schedules compared with traditional techniques. Nonetheless, the pace of OSC implementation has been slow due to the influence of several barriers. In recent years, virtual reality (VR) applications have been used to address many of these barriers and promote the implementation of OSC projects. However, a comprehensive and coherent literature review that establishes the current state and categorizes VR applications in OSC projects is still lacking. To address this research gap, this study provides a state-of-the-art review of VR applications in OSC (VR–OSC) using the scientometric and systematic review methods. This study characterizes the synthesis between VR and OSC and identifies research trends and gaps that can be studied in future VR–OSC research. The scientometric review focuses on identifying the main topics of both research domains separately and combined based on the collected articles. The systematic review, meanwhile, qualitatively evaluates these articles, highlighting the existing research gaps and anticipating future research frontiers. The scientometric results indicate that VR applications in OSC can be organized into a number of clusters, such as Crane Operations and Onsite Planning, Educational Applications, Safety and Ergonomics, and Evaluation of Design Alternatives. The qualitative analysis identifies several future research directions to advance the field of VR–OSC, including (1) multiuser VR models in crane operation planning, (2) consideration of the role of human emotions in VR safety training by adopting biometric sensors, (3) decentralized web-VR platforms for remote OSC planning, and (4) VR-solutions for modeling robotic movements in OSC factories. This study can serve as a useful point of reference for VR–OSC researchers and provides a sound foundation for future research on VR–OSC. Practical Applications: As the market share of OSC techniques continues to grow, it is vital for construction professionals to stay up to date with the latest advances and technologies so that they are well-positioned to identify and address adoption barriers. This study provides a comprehensive analysis of the two topics (i.e., OSC and VR) both independently and integrally. The review of VR applications and their categorization into several OSC subdomains, such as crane operations management, safety management, processes planning, and educational practices, provides OSC practitioners with a holistic understanding on the basis of which to employ VR solutions more efficiently and effectively in their projects. Furthermore, the integration of VR with other technologies, such as eye-tracking technologies, motion capture sensors, and robotic tools, illustrates how VR tools can be used to address many OSC-related problems, including complex production lines, inefficient safety training, and overlapping crane operations. Finally, the improved understanding of current gaps in the domain of VR–OSC can help to galvanize industry support for more in-depth research on the use of VR in OSC. [ABSTRACT FROM AUTHOR]

At present, the extraction of irrigation canal network distribution information is of great significance for developing a digital twin irrigation district. However, due to the low resolution of remote sensing images, it is difficult to effectively identify the canal networks, especially for channels with a width of less than 1 m, where recognition is insufficient. Therefore, the purpose of this study is to extract canal networks of different widths in an irrigation district in Shaanxi Province as the research area. A rule-based object-oriented classification method was employed, utilizing image data collected by the DJI Mavic 3 multispectral UAV (Unmanned Aerial Vehicle) to explore the accuracy of this method in extracting canal distribution information. Based on UAV multispectral remote sensing imagery, the segmentation parameters for the remote sensing imagery were determined using ENVI 5.6 software, with the segmentation threshold set at 60 and the merging threshold set at 80. By combining the spectral and spatial differences between the canals and other ground objects, rules for extracting canal network distribution information were established, and the information on the distribution of channels in this irrigation area was finally obtained. The experimental results showed a maximum recall rate of 91.88% and a maximum precision rate of 57.59%. The overall recall precision rates for the irrigation district were 85.74% and 55.08%, respectively. This method provides a new solution for identifying and extracting canal systems in irrigation districts, offering valuable insights for acquiring canal distribution information and providing a scientific basis for precision irrigation. [ABSTRACT FROM AUTHOR]

Digital Twin (DT) technology, as the evolution of Building Information Modeling (BIM), has emerged to address global concerns regarding the environmental impacts of the construction industry and to meet sustainability indicators. Despite numerous studies targeting the integration of DT and sustainability, there is a noticeable gap in creating a comprehensive overview of the efforts and future directions in this field. Therefore, this research aims to provide both a scientometric analysis and a thematic review of 235 papers extracted from the Scopus database. These papers, all published between 2017 and 2024, focus on previous efforts, current trends, and future directions of using the Digital Twin for construction sustainability. In addition, 34 papers that were cited more than 20 times were classified by the application into four categories: simulation, technology integration, smart systems, and literature review. Furthermore, regarding the application of smart systems in sustainability, the authors discussed applications of BIM-DT in smart construction, smart buildings, smart infrastructures, and smart cities based on the most-cited papers. Subsequently, five research gaps were identified and suggested for future investigation. The research gives a holistic insight into the current trend of DT among researchers, previous achievements, and future directions. [ABSTRACT FROM AUTHOR]

Three-dimensional virtual modeling is one of the tools being rapidly implemented in the construction industry, leading to the need for strategies based on intelligent 3D models of cities and/or digital twins, which allow simulation by interacting with their real physical counterparts, anticipating the outcomes of decision making. In practice, problems arise when creating and managing these twins, as different data, models, technology, and tools must be used, and they cannot all be combined as desired due to certain incompatibilities. On the other hand, today's traditional building management demands a more optimized process to prevent errors and enable timely reactions to failures and defects. Managing and using a large amount of complex and disparate data are required, which is why the use of CMMS-type software is common (Computerized Maintenance Management System). However, such software is rarely designed for management in a 3D format, often due to the absence of three-dimensional models of the assets. This research aims to contribute to the technological development of the digitalization of the built environment, providing a simple methodology for generating and managing 3D models of cities. To achieve this, the tools and information useful for generating an integrated GIS 3D and BIM model, and for Computer-Aided Maintenance Management in a three-dimensional format (CMMS-3D), are identified. The final model obtained is used to optimize the three-dimensional management of a classroom building on the "Campus de Las Llamas" at the University of Cantabria in Spain. The results demonstrate that it is possible to integrate digital models with simple linking mechanisms between the existing tools, thus achieving an optimal three-dimensional management model. [ABSTRACT FROM AUTHOR]

Digital Twin (DT) technologies have demonstrated a positive impact across various stages of the Architecture, Engineering, and Construction (AEC) industry. Nevertheless, the industry has been slow to undergo digital transformation. The paper utilizes the Systematic Literature Review (SLR) approach to study a total of 842 papers on the application of DT in buildings, landscapes, and urban environments (BLU) from 2018 to 2024. Based on the research results, suggestions have been made for future research and practical directions. Meanwhile, it provides assistance to BLU's designers, constructors, managers, and policymakers in establishing their understanding of the digital transformation of the AEC industry. The existing relevant research can be mainly divided into three categories: case study, framework study, and technology study. Compared with the buildings and urban environment industries, the number and depth of research in the landscape industry are relatively low. Through in-depth analysis of BLU projects, three research trends in the future are determined: (1) research and application of DT framework in the design and planning stage; (2) development of design tools and basic theory based on DT model; (3) application and exploration of DT technology in the landscape industry. [ABSTRACT FROM AUTHOR]

Building Information Modeling (BIM) has emerged as a transformative technology in the Architecture, Engineering, and Construction (AEC) industry, with increasing application in civil infrastructure projects. This study comprehensively reviews the research landscape of BIM applications in civil infrastructure through bibliometric analysis. Based on data from the Web of Science database, 646 relevant papers published between 2020 and 2024 were collected, and 416 papers were selected for in-depth analysis after screening. Using bibliometric methods, the analysis reveals the evolution of research trends, identifies key contributors and influential publications, and maps the knowledge structure of the field. Our study shows a significant increase in research output over the past five years, particularly in studies focusing on the integration of BIM with emerging technologies such as Digital Twins, the Internet of Things (IoT), and Machine Learning. The results indicate that the United States, China, and the United Kingdom lead in terms of research output and citation impact. Additionally, based on clustering results and representative keywords, several key research clusters were identified, including BIM in infrastructure lifecycle management, BIM collaboration in large-scale projects, and BIM for sustainable infrastructure design. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose a workflow for the real-time visualization of virtual environments that supports diagnostic tasks in heritage buildings. The approach integrates data from terrestrial laser scanning (3D point clouds and meshes), along with panoramic and thermal images, into a unified virtual model. Additionally, the methodology incorporates several post-processing stages designed to enhance the user experience in visualizing both the building and its associated damage. The methodology was tested on the Medieval Templar Church of Vera Cruz in Segovia, utilizing a combination of visible and infrared data, along with manually prepared damage maps. The project results demonstrate that the use of a hybrid digital model—combining 3D point clouds, polygonal meshes, and panoramic images—is highly effective for real-time rendering, providing detailed visualization while maintaining adaptability for mobile devices with limited computational power. [ABSTRACT FROM AUTHOR]

Building Information Modelling (BIM) integration with the Internet of Things (IoT) is progressing. The high level of BIM maturity involves using sensor data to manage processes or objects. The article presents the process of creating a telemetry connection between the BIM model and a Radio-Frequency Identification (RFID) sensor in the context of gaining access to various parts of a building. The process of creating a connection using an experimental set based on a microcontroller board for RFID reader support is described. The set was programmed using multiple programming languages and artificial intelligence. The article presents a unique process of connecting an RFID reader with BIM using a simple model that can be replicated in other contexts (e.g., gaining access to different parts of a construction site). Previous research shows that the unidirectional connection of IoT sensors with BIM models is not difficult. Instead, real-time bidirectional and stable connection (telemetry) is problematic. The authors undertook to fill this research gap using a proprietary IoT kit, programming sequence, and lightweight communication protocol. The paper makes a significant contribution to the discussion and understanding of BIM-IoT technology integration. The article also includes the limitations and possibilities for further development of such a connection. [ABSTRACT FROM AUTHOR]

This paper presents the design and development of a flexible hyper-distributed IoT–Edge–Cloud computing platform for real-time Digital Twins in real logistics and industrial environments, intended as a novel living lab and testbed for future 6G applications. It expands the limited capabilities of IoT devices with extended Cloud and Edge computing functionalities, creating an IoT–Edge–Cloud continuum platform composed of multiple stakeholder solutions, in which vertical application developers can take full advantage of the computing resources of the infrastructure. The platform is built together with a private 5G network to connect machines and sensors on a large scale. Artificial intelligence and machine learning are used to allocate computing resources for real-time services by an end-to-end intelligent orchestrator, and real-time distributed analytic tools leverage Edge computing platforms to support different types of Digital Twin applications for logistics and industry, such as immersive remote driving, with specific characteristics and features. Performance evaluations demonstrated the platform's capability to support the high-throughput communications required for Digital Twins, achieving user-experienced rates close to the maximum theoretical values, up to 552 Mb/s for the downlink and 87.3 Mb/s for the uplink in the n78 frequency band. Moreover, the platform's support for Digital Twins was validated via QoE assessments conducted on an immersive remote driving prototype, which demonstrated high levels of user satisfaction in key dimensions such as presence, engagement, control, sensory integration, and cognitive load. [ABSTRACT FROM AUTHOR]

5G Mobile Adhoc Networks (5G-MANETs) are a popular and agile solution for data transmission in local contexts while maintaining communication with remote entities via 5G. These characteristics have established 5G-MANETs as versatile communication infrastructures for deploying contextual applications, leveraging physical proximity while exploiting the possibilities of the Internet. As a result, there is growing interest in exploring the potential of these networks and their performance in real-world scenarios. However, the management and monitoring of 5G-MANETs are challenging due to their inherent characteristics, such as highly variable topology, unstable connections, energy consumption of individual devices, message routing, and occasional inability to connect to 5G. Considering these challenges, the proposed work aims to address real-time monitoring of 5G-MANETs using a connection-aware Digital Twin (DT). The approach provides two main functions: offering a live virtual representation of the network, even in scenarios where multiple nodes lack 5G connectivity, and estimating the performance of the infrastructure, enabling the specification of customized conditions. To achieve this, a communication architecture is proposed, analyzing its components and defining the involved processes. The DT is implemented and evaluated in a laboratory setting, assessing its accuracy in representing the physical network under varying conditions of topology and Internet availability. The results show 100% accuracy for the DT in fully connected topologies, with ultra-low latency averaging under 80 ms, and suitable performance in partially connected contexts, with latency averages below 3000 ms. [ABSTRACT FROM AUTHOR]

Robotics and automation are the key industries supporting Thailand's strategic initiatives toward Thailand 4.0. This paper presents the development of an industry roadmap guiding the future research and development of related technologies in Thailand. The roadmap project was recently conducted during 2021–2022. The strategic targets for the future development of robotics and automation in Thailand were set to increase productivity and reduce adoption cost for the short term (2022–2023); create market opportunities for AI and industrial IoT technologies with data platform for the medium term (2024–2026); and prepare the Industry 5.0 system supporting emerging applications of digital twins and metaverse for the long term (2027–2030). [ABSTRACT FROM AUTHOR]

This study addresses the challenge of automating the creation of Exchange Information Requirements (EIRs) for construction projects using Building Information Modelling (BIM) and Digital Twins, as specified in the ISO 19650 standard. This paper focuses on automating the classification of EIR paragraphs according to the ISO 19650 standard's categories, aiming to improve information management in construction projects. It addresses a gap in applying AI to enhance BIM project management, where barriers often include technological limitations, a shortage of specialists, and limited understanding of the methodology. The proposed method uses Word2Vec for text vectorisation and Support Vector Machines (SVMs) with an RBF kernel for text classification, and it attempts to apply Word2Vec with cosine similarity for text generation. The model achieved an average F1 score of 0.7, with predicted categories for provided sentences and similar matches for selected phrases. While the text classification results were promising, further refinement is required for the text generation component. This study concludes that integrating AI tools such as Word2Vec and SVM offers a feasible solution for enhancing EIR creation. However, further development of text generation, particularly using advanced techniques such as GPT, is recommended. These findings contribute to improving managing complex construction projects and advancing digitalization in the AECO sector. [ABSTRACT FROM AUTHOR]

Electric mobility is a global trend and necessity, with electric and solar boats offering a promising alternative for transportation electrification and carbon emission reduction, especially in the Amazon region. This study analyzes the system of a solar boat from an electric mobility project—to be implemented at Universidade Federal do Pará (UFPA)—using MATLAB software for modeling. The Simulink tool was utilized to model the system, focusing on operational parameters such as module voltage, converter voltage, and speed. The results indicate that the solar boat's operational cost is significantly lower compared to a similar internal combustion model, considering diesel's high consumption and cost. The environmental impact is also reduced, with nearly 72 tons of CO2 emissions avoided annually, thanks to Brazil's renewable energy matrix. Simulations confirmed the project's parameters, demonstrating the efficiency of digital-twin technology in monitoring and predicting system performance. The study underscores the importance of digital twins and renewable energy in promoting sustainable transportation solutions, advocating for the replication of such projects globally. Future research should focus on further advancing digital-twin applications in electric mobility to enhance predictive maintenance and operational efficiency. [ABSTRACT FROM AUTHOR]

The research presented in this paper aligns with the advancement of Industry 4.0 by integrating intelligent machine tools and industrial robots within Flexible Manufacturing Systems (FMS). Primarily, a development approach for Digital Twin (DT) is presented, beginning from the design, sizing, and configuration stages of the system and extending through its implementation, commissioning, operation, and simulation-based optimization. The digitization of current industrial processes entails the development of applications based on modern technologies, utilizing state-of-the-art tools and software. The general objective was to create a digital replica of a process to propose optimization solutions through simulation and subsequently achieve virtual commissioning. The practical nature of the research is reflected in the design and implementation of a Digital Twin for a real physical system processing a family of cylindrical parts within an existing experimental FMS. A digital model of the system was created by defining each individual device and piece of equipment from the physical system, so the virtual model operates just like the real one. By implementing the Digital Twin, both time-based and event-based simulations were performed. Through the execution of multiple scenarios, it was possible to identify system errors and collisions, and propose optimization solutions by implementing complex, collaborative-robot equipment where multiple interactions occur simultaneously. [ABSTRACT FROM AUTHOR]

As information technology progresses, the need for digital transformation within the industrial sector has become increasingly apparent, and digital twin technology has emerged as a significant trend in manufacturing. Digital twins synchronize physical and digital environments, overcoming spatial and temporal limitations to create various added values that are unattainable in reality. This paper presents a model that integrates digital twin technology with production and operational technologies at manufacturing sites, enabling remote, centrally controlled manufacturing services that transcend physical constraints. Specifically, by utilizing Message Queuing Telemetry Transport (MQTT) for real-time synchronization, this approach ensures efficient and timely data transfer between physical and digital environments. While traditional approaches often encounter challenges due to high investment costs and design complexities, this paper proposes a cost-effective and practical solution that reflects actual factory conditions. [ABSTRACT FROM AUTHOR]

Continuous crop monitoring enables the early detection of field emergencies such as pests, diseases, and nutritional deficits, allowing for less invasive interventions and yielding economic, environmental, and health benefits. The work organization of modern agriculture, however, is not compatible with continuous human monitoring. ICT can facilitate this process using autonomous Unmanned Ground Vehicles (UGVs) to navigate crops, detect issues, georeference them, and report to human experts in real time. This review evaluates the current state of ICT technology to determine if it supports autonomous, continuous crop monitoring. The focus is on shifting from traditional cloud-based approaches, where data are sent to remote computers for deferred processing, to a hybrid design emphasizing edge computing for real-time analysis in the field. Key aspects considered include algorithms for in-field navigation, AIoT models for detecting agricultural emergencies, and advanced edge devices that are capable of managing sensors, collecting data, performing real-time deep learning inference, ensuring precise mapping and navigation, and sending alert reports with minimal human intervention. State-of-the-art research and development in this field suggest that general, not necessarily crop-specific, prototypes of fully autonomous UGVs for continuous monitoring are now at hand. Additionally, the demand for low-power consumption and affordable solutions can be practically addressed. [ABSTRACT FROM AUTHOR]

The use of digital twin and shadow concepts for industrial material processes has introduced new approaches to bridge the gap between physical and cyber manufacturing processes. Consequently, many multidisciplinary areas, such as advanced sensor technologies, material science, data analytics, and machine learning algorithms, are employed to create these hybrid systems. Meanwhile, new additive manufacturing (AM) processes for metals and polymers, based on emerging technologies, have shown promise for the manufacturing of sophisticated parts with complex geometries. These processes are undergoing a major transformation with the advent of digital technology, hybrid physical-data-driven modeling, and fast-reduced models. This study presents a fresh perspective on hybrid physical-data-driven and reduced order modeling (ROM) techniques for the digitalization of AM processes within a digital twin concept. The main contribution of this study is to demonstrate the benefits of ROM and machine learning (ML) technologies for process data handling, optimization/control, and their integration into the real-time assessment of AM processes. Therefore, a novel combination of efficient data-solver technology and an architecturally designed neural network (NN) module is developed for transient manufacturing processes with high heating/cooling rates. Furthermore, a real-world case study is presented, showcasing the use of hybrid modeling with ROM and ML schemes for an industrial wire arc AM (WAAM) process. [ABSTRACT FROM AUTHOR]

The digital twins process of equipment operation and maintenance involves massive data and complex business logic. Through data awareness of equipment and workshop environment. According to the characteristics of equipment operation and maintenance, the data characteristics and requirements are analysed, an equipment operation and maintenance management system based on the micro-service framework is proposed, a set of equipment operation and maintenance digital twins are designed and a general reference software architecture is designed and by checking the underlying in-heap pattern reduces high-impact portions of memory to provide accurate follow-up complex digital twins modelling provides the architectural foundation. [ABSTRACT FROM AUTHOR]

Wind power generation has become an indispensable part of the power supply side of the power grid. Due to the intermittent and uncertain characteristics of wind energy, short-term wind power prediction plays an important role in the stable operation of power system. By constructing the digital twin model, real-time and high-precision prediction of wind energy is realized. First, the genetic algorithm-support vector machine (GA-SVM) algorithm is used to build the model. Multidimensional sensors and meteorological stations of the virtual wind power generation system collected the meteorological data of the environment and updated the meteorological history database at the same time. Second, based on the collected meteorological data, the preliminary prediction results are obtained, and by searching in the historical database, the predicted value and the actual output value of wind turbines or wind farms under similar conditions are obtained. Finally, the prediction results of the GA-SVM are modified to obtain the predicted value of the digital twin model. The prediction method can greatly improve the accuracy of the short-term forecast of wind energy. [ABSTRACT FROM AUTHOR]

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Accurate and efficient modeling and simulation of spacecraft electrical systems are crucial because of their complexity. However, existing models often struggle to balance simulation efficiency and accuracy. This paper introduces a model reduction method based on singular perturbation theory to simplify the full-order model of spacecraft electrical systems. The experimental results show that the reduced-order simplified model saves 50% of the simulation time with almost no degradation in the simulation accuracy and can be applied to real-world scenarios, such as digital twins. This method offers a new approach for rapid simulation of spacecraft electrical systems and has broad application prospects. [ABSTRACT FROM AUTHOR]

This article deals with the design and creation of digital twins of robotic stations. A literature review of digital twins, robot programming methods and laser tracker applications is presented. This paper shows that the construction of digital twins is closely related to one of the most popular methods of robot programming, i.e., off-line programming. In the case of digital twins of robotic stations, modeling accuracy and two-way communication with the real station proved to be crucial. The article proposes a methodology for solving the basic problem of off-line robot programming, i.e., the limited accuracy of the representation of the station and the details. The algorithm of proceeding in the case when the station already exists and its digital model is built and the case when the digital model is first created and the real solution is built on its basis is shown. According to the developed methodology, a digital twin of a real robotic station was created and the possibilities arising from the use of virtual tools were shown. The developed digital twin has the ability to communicate with advanced Matlab 2021-type tools, uses cloud solutions and virtual and augmented reality for training, simulates physical phenomena and provides the ability to accurately program robots off-line. [ABSTRACT FROM AUTHOR]

The dynamic performances of an innovative robotic total station (RTS) are assessed while tracking a moving target; also, the results of these performance evaluations are exploited to design its control system. The proposed methodology is novel and uses ad hoc virtual experiments implemented on the digital twin of the studied RTS. This methodology is general and is also applicable for the design of other mechatronic systems. The proposed ad hoc virtual experiments constitute a benchmark for assessing the tracking performances of pointing systems. The application of this methodology to the studied RTS will help complete the RTS's design by determining the most suitable actuators and the control system constants necessary to obtain the assigned dynamic performances. A 3D-printed prototype of the designed RTS was built and is shown in this paper. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present examples and original solutions related to the application of the digital twin concept in designing safety systems for robotic stations. This paper includes a review of publications on robot safety systems and digital twins. Based on this review, it was concluded that further work in this area is justified. This paper demonstrates the use of a digital model of a robotic casting mold preparation station to design safety components for an industrial cell. A key element of this paper is the presentation of developed algorithms and their applications in building digital twins of existing robotic stations. By characterizing advanced safety systems used in robotic stations, an example of using a digital twin of a robotic station to create safety zones and so-called restricted zones for the robot was developed. As part of the research conducted, a real, comprehensive example of creating safety zones based on the robot's TCP paths was carried out. [ABSTRACT FROM AUTHOR]

Side-channel attacks (SCAs) are powerful techniques used to recover keys from electronic devices by exploiting various physical leakages, such as power, timing, and heat. Although heat is one of the less frequently analyzed channels due to the high noise associated with thermal traces, it poses a significant and growing threat to the security of very large-scale integrated (VLSI) microsystems, particularly system in package (SiP) technologies. Thermal side-channel attacks (TSCAs) exploit temperature variations, risking not only hardware damage from excessive heat dissipation but also enabling the extraction of sensitive data, like cryptographic keys, by observing thermal patterns. This dual threat underscores the need for a synergistic approach to thermal management and security in designing integrated microsystems. In response, this paper presents a novel approach that improves the early detection of abnormal thermal fluctuations in SiP designs, preventing cybercriminals from exploiting such anomalies to extract sensitive information for malicious purposes. Our approach employs a new concept called Thermal Digital Twin (TDT), which integrates two previously separate methods and techniques, resulting in successful outcomes. It combines the gradient direction sensor scan (GDSSCAN) to capture thermal data from the physical field programmable gate array (FPGA), which guarantees rapid thermal scan with a measurement period that could be close to 10   μ s , a resolution of 0.5   ∘ C, and a temperature range from − 40   ∘ C to 140   ∘ C; once the data are transmitted in real time to a Digital Twin created in COMSOL Multiphysics® 6.0 for simulation using the Finite Element Method (FEM), the real time required by the CPU to perform all the necessary calculations can extend to several seconds or minutes. This integration allows for a detailed analysis of thermal transfer within the SiP model of our FPGA. Implementation and simulations demonstrate that the Thermal Digital Twin (TDT) approach could reduce the risks associated with TSCA by a significant percentage, thereby enhancing the security of FPGA systems against thermal threats. [ABSTRACT FROM AUTHOR]

Multi-user collaborative robotic simulation has great potential for applications in industry and education. Unity is a powerful software for simulation and online multi-user experience, which can be enhanced with third-party mathematical analysis and multiplayer servers. Unity can become a much more capable and user-friendly robotic simulation package through integration with other software. These include MATLAB for computations and the Photon Unity Engine (PUN) for online multi-user capabilities. This study developed a flexible robotic simulation framework that can be adapted to different scenarios for industrial and educational applications. Several simulation scenarios were developed to identify the most efficient data communication methods between MATLAB and Unity. TCP/IP, Shared Memory, Firebase, and MQTT, were selected to assess their performance and interaction with data in Unity and MATLAB. Next, an independent PUN application was created. Both components were integrated into the simulator for evaluation and performance optimization. The performance of this simulation framework was assessed through two case studies. The results demonstrated that the integrated framework is viable, efficient, and flexible for robotic simulation and digital twins. Future research will expand the framework by adding diverse functionalities to provide users with a better interface, enhancing its performance, and integrating additional software packages. [ABSTRACT FROM AUTHOR]

The Digital Twin (DT) technology has shown promise in transforming complex engineered systems. However, its adoption in the Architecture, Engineering, Construction, and Operation (AECO) field, particularly for Built Cultural Heritage (BCH) conservation, is still in its early stages. This study presents a systematic literature review of 85 academic publications to evaluate the current state of DT implementation in heritage building conservation and identifies areas for optimising preventive management. In addition, this review explores interpretations of the DT concept in this field, addressing overlaps with Historical/Heritage Building Information Modelling (HBIM), discusses DT functionalities and presents existing frameworks. The findings demonstrate the potential of DT to revolutionise BCH conservation through a holistic approach. However, further focus is needed on features and tools for enhancing performance-based management with targeted strategies and advanced data analysis. Future research should prioritise developing these aspects to fully leverage the potential of the DT paradigm in BCH conservation. [ABSTRACT FROM AUTHOR]

A Distributed Intelligent System (DIS) encompasses a set of intelligent subsystems and components that collaborate to perform tasks and solve problems. Given the advancements of paradigms such as the Internet of Things, along with the advancements of technologies such as Machine Learning and Digital Twins, DISs are on the rise. These systems are increasingly integrating components that perform intelligent functions, and these intelligent functions are increasingly heterogeneous and varied. Moreover, there is no standardized framework to help researchers and practitioners adequately address DISs. As a result, the complexity, interoperability issues, and development time and costs of these systems are growing. However, Model-Driven Development (MDD) can help to address these challenges by providing a Domain-Specific Language (DSL) for developing DISs. In this work, a DSL for the design, validation, generation, and deployment of DISs is proposed. Firstly, the proposed DSL captures in a metamodel the key and high-level abstract concepts of the distinct DISs documented in the literature. Then, it allows modeling of DISs conforming to this metamodel. Subsequently, the DSL enables formal validation of the modeled systems. Lastly, it allows the generation and deployment of all DISs into production. Therefore, the work undertaken in this communication provides a methodological, formal, and standardized approach to defining and developing DISs from a high level of abstraction. This work allows users to address DISs by facilitating agility, minimizing manual tasks, and reducing the number of defects introduced in their development. To illustrate the applicability of the proposed DSL, a real case study of an agricultural digital twin is presented. [ABSTRACT FROM AUTHOR]

In the context of enhanced decision making related to Industry 4.0 and 5.0, this work examines the first step toward the implementation of a Digital Twin (DT) in a discrete manufacturing firm. It will be required that the DT be adequately integrated with the information systems, especially the Manufacturing Execution System (MES), because the virtual counterpart of the DT itself, a Discrete Event Simulator (DES) model, will exploit the MES data for the validation and monitoring. The objective of the DT is to enhance the decision making related to production planning in particular, achieving better on-time delivery to customers. Therefore, the DT intends to depict material flows within the production department to enhance the monitoring and control, facilitating the prompt identification of deviations from the plan and supporting the decision-makers, enabling a more responsive and informed management of delay alerts. The first goal to achieve the DT implementation and integration is to establish a conceptual framework that improves material flow data synchronization. A conceptual integration and implementation framework for the DT will be proposed and discussed, underlying the technical decisions chosen to achieve the functional and integration requirements. [ABSTRACT FROM AUTHOR]

In recent years, digital twin (DT) technology has garnered significant interest from both academia and industry. However, the development of effective fault detection and diagnosis models remains challenging due to the lack of comprehensive datasets. To address this issue, we propose the use of Generative Adversarial Networks (GANs) to generate synthetic data that replicate real-world data, capturing essential features indicative of health-related information without directly referencing actual industrial DT systems. This paper introduces an intelligent fault detection and diagnosis framework for industrial triplex pumps, enhancing fault recognition capabilities and offering a robust solution for real-time industrial applications within the DT paradigm. The proposed framework leverages Conditional GANs (CGANs) alongside the Harris Hawk Optimization (HHO) as a metaheuristic method to optimize feature selection from input data to enhance the performance of machine learning (ML) models such as Bagged Ensemble (BE), AdaBoost (AD), Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), Decision Tree (DT), and Naive Bayes (NB). The efficacy of the approach is evaluated using key performance metrics such as accuracy, precision, recall, and F-measure on a triplex pump dataset. Experimental results indicate that hybrid-optimized ML algorithms (denoted by "ML-HHO") generally outperform or match their classical counterparts across these metrics. BE-HHO achieves the highest accuracy at 95.24%, while other optimized models also demonstrate marginal improvements, highlighting the framework's effectiveness for real-time fault detection in DT systems, where SVM-HHO attains 94.86% accuracy, marginally higher than SVM's 94.48%. KNN-HHO outperforms KNNs with 94.73% accuracy compared to 93.14%. Both DT-HHO and DT achieve 94.73% accuracy, with DT-HHO exhibiting slightly better precision and recall. NB-HHO and NB show near-equivalent performance, with NB-HHO at 94.73% accuracy versus NB's 94.6%. Overall, the optimized algorithms demonstrate consistent, albeit marginal, improvements over their classical versions. [ABSTRACT FROM AUTHOR]

The rapid reconstruction of the internal flow field within pressure vessel equipment based on features from limited detection points was of significant value for online monitoring and the construction of a digital twin. This paper proposed a surrogate model that combined Proper Orthogonal Decomposition (POD) with deep learning to capture the dynamic mapping relationship between sensor monitoring point information and the global flow field state during equipment operation, enabling rapid reconstruction of the temperature field and velocity field. Using POD, the order of the tested temperature field was reduced by 99.75%, and the order of the velocity field was reduced by 99.13%, effectively decreasing the dimensionality of the flow field. Our analysis revealed that the first modal coefficient of the temperature field snapshot data, after modal decomposition, had a higher energy proportion compared to that of the velocity field snapshot data, along with a more pronounced marginal effect. This indicates that more modes need to be retained for the velocity field to achieve a higher total energy proportion. By constructing a CSSA-BP model to represent the mapping relationship between the modal coefficients of the temperature and velocity fields and the data collected from the detection points, a comparison was made with the BP method in reconstructing the temperature field of a shell-and-tube heat exchanger. The CSSA-BP method yielded a maximum mean squared error (MSE) of 9.84 for the reconstructed temperature field, with a maximum mean absolute error (MAE) of 1.85. For the velocity field, the maximum MSE was 0.0135 and the maximum MAE was 0.0728. The global maximum errors for the reconstructed temperature field were 4.85%, 3.65%, and 4.29%, respectively. The global maximum errors for the reconstructed velocity field were 17.72%, 11.30%, and 16.79%, indicating that the model established in this study has high accuracy. Conventional CFD simulation methods require several hours, whereas the reconstruction model proposed here can rapidly reconstruct the flow field within 1 min after training is completed, significantly reducing reconstruction time. This work provides a new method for quickly obtaining the internal flow field state of pressure vessel equipment under limited detection points, offering a reference for online monitoring and the development of digital twins for pressure vessel equipment. [ABSTRACT FROM AUTHOR]

OVERVIEW: The artificial intelligence (AI) revolution in new product development (NPD) has reached a critical juncture, demanding decisive action from business leaders. Our comprehensive study of key AI applications in NPD reveals a startling lack of adoption across industries, particularly in the United States. Despite the potential benefits, businesses also demonstrate weak intentions to adopt AI and low readiness regarding AI integration into NPD processes. Despite the low adoption rate, the study's results offer a compelling picture of AI's potential, including substantial positive impacts of AI use in key areas like product design, prototyping, testing, digital twin simulations, market analysis, and new product ideation. Benefits include accelerated development and markedly improved decision-making. PRACTITIONER TAKEAWAYS: The current adoption rate of AI for NPD is low, as is the intent to adopt, especially in the United States. Primary reasons for hesitancy include lack of management commitment, low demonstrated value, and lack of trust. Strong positive impact is evident between specific AI applications in the development and testing stages of the NPD process and critical NPD performance metrics. Firms should make a concerted effort to accelerate their AI adoption for NPD. The initiative must be led by strong commitment from senior leadership and focus on value creation. Firms must use a proven technology adoption process to guide AI implementation efforts and avoid common pitfalls. [ABSTRACT FROM AUTHOR]

Disassembly is a key step in remanufacturing, especially for end-of-life (EoL) products such as electric vehicle (EV) batteries, which are challenging to dismantle due to uncertainties in their condition and potential risks of fire, fumes, explosions, and electrical shock. To address these challenges, this paper presents a robotic teleoperation system that leverages augmented reality (AR) and digital twin (DT) technologies to enable a human operator to work away from the danger zone. By integrating AR and DTs, the system not only provides a real-time visual representation of the robot's status but also enables remote control via gesture recognition. A bidirectional communication framework established within the system synchronises the virtual robot with its physical counterpart in an AR environment, which enhances the operator's understanding of both the robot and task statuses. In the event of anomalies, the operator can interact with the virtual robot through intuitive gestures based on information displayed on the AR interface, thereby improving decision-making efficiency and operational safety. The application of this system is demonstrated through a case study involving the disassembly of a busbar from an EoL EV battery. Furthermore, the performance of the system in terms of task completion time and operator workload was evaluated and compared with that of AR-based control methods without informational cues and 'smartpad' controls. The findings indicate that the proposed system reduces operation time and enhances user experience, delivering its broad application potential in complex industrial settings. [ABSTRACT FROM AUTHOR]

Air exchange in pressurized water pipelines is an essential but complex aspect of pipeline modeling and operation. Implementing effective air management strategies can yield numerous benefits, enhancing the system's energy efficiency, reliability, and safety. This paper comprehensively evaluates an irregular profile pipeline filling procedure involving air-release through an air valve. The analysis includes real-time data tests and numerical simulations using Computational Fluid Dynamics (CFD). A Digital Twin model was proposed and applied to filling maneuvers in water installations. In particular, this research considers an often-overlooked aspect, such as filling a pipe with an irregular profile rather than a simple straight pipe. CFD simulations have proven to capture the main features of the transient event, which are suitable for tracking the air-water interface, the unsteady water flow, and the evolution of the trapped air pocket. Thus, they provide thorough and reliable information for real-time operational processes in the industry, focusing on the filling pressure and geometry of the air-valve hydraulic system. Additionally, this study provides details regarding the application of an efficient Digital Twin CFD approach, demonstrating its feasibility in optimizing the filling procedure in pipes with irregular profiles. [ABSTRACT FROM AUTHOR]

ChatGPT uses a so called Large Language Model (LLM) to provide textual output of analyzed data. Those LLMs are one example for Generative Artificial Intelligence (AI), which focuses on creating new content, e.g., text, images, or music, based on learned patterns. Recently, applications in the food industry and agriculture started to apply Generative AI. This mini review provides an overview about applications of Generative AI in the agri-food supply chain and discusses open research challenges, also in combination with digital twins. [ABSTRACT FROM AUTHOR]

We reflect on the development of digital twins of the Earth, which we associate with a reductionist view of nature as a machine. The projects of digital twins deviate from contemporary scientific paradigms in the treatment of complexity and uncertainty, and does not engage with critical and interpretative social sciences. We contest the utility of digital twins for addressing climate change issues and discuss societal risks associated with the concept, including the twins' potential to reinforce economicism and governance by numbers, emphasizing concerns about democratic accountability. We propose a more balanced alternative, advocating for independent institutions to develop diverse models, prioritize communication with simple heuristic‐based models, collect comprehensive data from various sources, including traditional knowledge, and shift focus away from physics‐centered variables to inform climate action. We argue that the advancement of digital twins should hinge on stringent controls, favoring a nuanced, interdisciplinary, and democratic approach that prioritizes societal well‐being over blind pursuit of computational sophistication. This article is categorized under:Climate Models and Modeling > Earth System ModelsClimate Models and Modeling > Knowledge Generation with ModelsClimate, History, Society, Culture > Disciplinary Perspectives [ABSTRACT FROM AUTHOR]

A digital process was developed to facilitate additive manufacturing for ceramic materials using digital light processing (DLP). A numerical model that predicts DLP sample properties can be generated from manufacturing inputs to forecast the effect of resin age on mechanical strength of the printed part based on data collected from experiments. Key parameters for printing the green bodies included determining the depth of cure, layer thickness, material composition, and solids loading. Thermogravimetric analyses were used to develop debinding and sintering curves. Debinding is used to remove the volatile organics comprising the photopolymer resin. Sintering is performed after debinding to increase density and mechanical strength of the printed parts. The sintered parts were then subjected to characterization and mechanical testing. The ensemble of data for various DLP-printed ceramic materials were added to a database. A design of experiments can be generated from the manufacturing process defined in the database with selected changeable parameters randomized over a range. Because the database is defined with an architecture to capture manufacturing processes, it can persist as a more generic platform for manufacturing digital twins. This can ease the development of future digital twins and can grow as a common repository for the insights gained from manufacturing research. Creating a digital twin of a DLP system for 3D printing parts enables manufacturers to simulate and assess the impact of resin age on printing parameters and part quality, facilitating optimization, predictive maintenance, and cost reduction. [ABSTRACT FROM AUTHOR]

Airports have ambitious decarbonization objectives, with many EU airports pledging to reach net-zero carbon emissions by 2050 for all emissions fully under their control. This requires setting a baseline and defining roadmaps composed of decarbonization measures that intend to reduce energy demand and carbon emissions from that baseline. However, there is substantial uncertainty in defining the impact that can be achieved through these roadmaps, which can be substantially reduced through the use of a digital twin. In this paper, we present the creation of a baseline digital twin of the built environment of Brussels Airport, which will be used to analyze the impact of decarbonization measures before they are deployed. To create the digital twin, an extensive data collection exercise was carried out to ensure that the created model is a realistic representation of the core airport systems and that its simulations are accurate in forecasting results. Utilizing the IES Apache physics-based engine, the digital twin predictions have been validated against actual energy consumption data. The present work demonstrates the methodology and results achieved, offering insights on how to tackle the complex challenge of creating a digital twin of the built environment of an entity as complex as an airport while focusing the baselining efforts on the most significant aspects, with the ultimate objective of reducing uncertainty of future decarbonization investments. The digital twin has been used to calculate the energy consumption and CO2 emission baseline, and it will be utilized to model key decarbonization measures that are part of the Brussels Airport net zero carbon roadmap. [ABSTRACT FROM AUTHOR]

This paper explores the telemedicine possibilities available to astronauts during spaceflight, focusing on the possible utilization of medical avatars for continuous health monitoring. Various types and sources of telemedicine are examined, along with their applications in deep space travel to address the challenges faced by astronauts and facilitate timely intervention in case of injuries and illnesses. The use of medical avatars presents a promising solution to mitigate risks and ensure effective healthcare delivery in the absence of immediate physical medical support. Furthermore, potential terrestrial applications of telemedicine and virtual medical avatars are discussed, highlighting the wider implications and benefits beyond space exploration. By leveraging advanced technologies and innovative approaches, telemedicine in conjunction with medical avatars offers a compelling avenue for enhancing healthcare practices both in space and on Earth. [ABSTRACT FROM AUTHOR]

Ports are of great significance in processing cargo containers and facilitating global marine logistics. Nevertheless, the susceptibility of the container shipping network for hazardous cargo is likely to intensify in the event of a significant disruption at a major port, such as adverse weather conditions, inadequate management practices, or unforeseen catastrophes. Such situations require the deployment of port protection emergency response and prevention in advance. This study proposes a digital twin (DT) model that employs extensive and trajectory data within containers to comprehensively analyze the occurrence of hazardous cargo failures within the port storage process. The virtual models of physical entities in the port are created through a data-driven approach, and the behavior of these entities in a port environment with big data is then simulated. A combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) algorithm is employed to provide predictions for the service layer of the DT system. The predicted correlation coefficients of temperature and humidity in the container reach 0.9855 and 0.9181, respectively. The developed system driven by DT models integrated with a CNN and the LSTM algorithm can more effectively assist the safety manager in achieving prevention in port operations. This study enables marine authorities and decision-makers to optimize emergency procedures, thereby reducing the probability of accidents in port operations and logistics. [ABSTRACT FROM AUTHOR]

The floor constitutes one of the largest areas within a building with which users interact most frequently in daily activities. Employing floor sensors is vital for smart‐building digital twins, wherein triboelectric nanogenerators demonstrate wide application potential due to their good performance and self‐powering characteristics. However, their sensing stability, reliability, and multimodality require further enhancement to meet the rapidly evolving demands. Thus, this work introduces a multimodal intelligent flooring system, implementing a 4 × 4 floor array for multimodal information detection (including position, pressure, material, user identity, and activity) and human–machine interactions. The floor unit incorporates a hybrid structure of triboelectric pressure sensors and a top‐surface material sensor, facilitating linear and enhanced sensitivity across a wide pressure range (0–800 N), alongside the material recognition capability. The floor array is implemented by an advanced output‐ratio method with minimalist output channels, which is insensitive to environmental factors such as humidity and temperature. In addition to multimodal sensing, energy harvesting is co‐designed with the pressure sensors for scavenging waste energy to power smart‐building sensor nodes. This developed flooring system enables multimodal sensing, energy harvesting, and smart‐sport interactions in smart buildings, greatly expanding the floor sensing scenarios and applications. [ABSTRACT FROM AUTHOR]

With the rapid advancement of Digital Twins (DTs) technologies, they have emerged as viable solutions for enhancing the control and optimization of thermal power combustion processes. This paper presents a pioneering approach that leverages DTs to revolutionize the combustion process control system within thermal power generating units. Focused on bridging the physical and digital realms, our research establishes DTs of the combustion process control system (CPCS), situated within the Networked Control System Laboratory (NCSLab). We propose a novel five-dimensional (5D) DTs model encompassing the physical, data, service, connection, and virtual models, thereby facilitating a comprehensive fusion of physical and digital information. By analyzing the demands and technical intricacies of the combustion process control system, we design and implement a virtual 3D model, enriching the visualization and analytical capabilities of the DT environment. Furthermore, through controlled experiments on the combustion process and rigorous comparison with offline simulations, we validate the efficacy and feasibility of our approach. Importantly, our research opens avenues for future exploration, particularly in harnessing industrial real-time data to drive the virtual 3D model. Through the integration of Artificial Intelligence (AI) techniques such as Deep Learning, we elevate the capabilities of our DT framework. This enhanced predictive analytics, dynamic optimization, human–machine interaction, and virtual model realism. This research contributes to the advancement of DT technology in the context of thermal power plants and holds promise for driving innovation and efficiency across diverse industrial sectors. [ABSTRACT FROM AUTHOR]

In track cycling, performance in the team pursuit depends on the mechanical and physiological abilities of each member of the team, but also on the choice of racing strategy. Athletes must cover the 4000 m of the race, sharing the effort between them in successive relays. This raises the question of the optimum strategy. We propose a method for resolving this question by coupling a mechanical model of the race to physiological models (digital twins) of the athletes. The mechanical model enables one to predict a theoretical finishing time for a given strategy, while the physiological model enables one to determine whether or not a given strategy is feasible. By coupling the two models and using numerical optimization, an optimal strategy for a given team can then be predicted. Simplified team composition case studies are explored. For each case studied, an optimal strategy to maximize performance is obtained and composed of a set of three variables: relay lengths, power values for each relay, and the starting order of cyclists. The proposed method can be used for real athletes and extended to other disciplines. [ABSTRACT FROM AUTHOR]

As the power industry becomes increasingly complex, Digital Twin (DT) technology has emerged as a crucial tool for enhancing grid resilience and operational efficiency by creating dynamic digital replicas of physical systems. These replicas enable accurate simulations and proactive management, but the vast amount of data generated by DT systems poses significant challenges for processing and analysis. Cloud computing offers a flexible solution by offloading these computational tasks to distributed resources, allowing for real-time analysis and scalable operations. However, this approach introduces complexities in task distribution and maintaining quality of service. Recent efforts have applied Deep Reinforcement Learning (DRL) to address these challenges, primarily using single-agent methods. However, these methods struggle with scalability and performance in increasingly complex cloud environments. To overcome these limitations, we propose an efficient task scheduling framework based on Multi-Agent Deep Q-Network (MADQN) principles, specifically designed to optimize both response times and operational costs. We provide a comprehensive design overview of our approach and conduct a thorough evaluation of its performance. The experimental results clearly indicate that our approach can considerably reduce response times amd lower operational costs compared to current methods, including state-of-the-art single-agent approaches. [ABSTRACT FROM AUTHOR]

IoT sensors are crucial for visualizing multidimensional and multimodal information and enabling future IT applications/services such as cyber-physical spaces, digital twins, autonomous driving, smart cities and virtual/augmented reality (VR or AR). However, IoT sensors need to be battery-free to realistically manage and maintain the growing number of available sensing devices. Here, we provide a novel sensor design approach that employs metasurfaces to enable multifunctional sensing without requiring an external power source. Importantly, unlike existing metasurface-based sensors, our metasurfaces can sense multiple physical parameters even at a fixed frequency by breaking classic harmonic oscillations in the time domain, making the proposed sensors viable for usage with limited frequency resources. Moreover, we provide a method for predicting physical parameters via the machine learning-based approach of random forest regression. The sensing performance was confirmed by estimating the temperature and light intensity, and excellent determination coefficients larger than 0.96 were achieved. Our study affords new opportunities for sensing multiple physical properties without relying on an external power source or requiring multiple frequencies, which markedly simplifies and facilitates the design of next-generation wireless communication systems. Metasurface-based sensors provide a battery-free sensing solution for maintaining numerous IoT devices with little human resources. However, the conventional method exploited resonant mechanisms associated with multiple physical parameters through different frequencies, although available frequencies were strictly limited. We report the first sensor design approach using circuit-based metasurfaces that offer a higher degree of freedom to design time-varying scattering profiles associated with multiple physical properties at a single frequency. Our prototype detects light intensity and temperature with an excellent determination coefficient above 0.96 via a machine-learning technique. [ABSTRACT FROM AUTHOR]

Digitalization is a key concept that transformed the various industries through technologies like Internet of Things (IoT), Artificial Intelligence (AI), and Digital Twin (DT). Although innovations provided by the advancement of digitalization have paved the way for more efficient operations and products for transportation, the rail transportation sector struggles to keep up with the rest of the transportation industry, since trains are designed to last for decades, and the insufficient infrastructure investment leads to multiple railroad derailments across the globe. Therefore, the primary aim is to transform current railway systems into human-centric, adaptable, sustainable and future-proof networks, aligning with Industry 5.0 (I5.0) and Circular Economy (CE) model supported by the restorative and long-lasting design of the trains. This transformation necessitates leveraging digitalization and emerging technologies to address the needs of passengers, operators, and maintenance personnel. This article provides a comprehensive review focusing on the application of IoT, AI, CE principles, and digital twin trains to existing railway infrastructure and assets. The analysis delves into developing system architecture for proposed solutions and their impact on operation, maintenance, sustainability, and passenger comfort, supported by track record analysis. The integration of these technologies and concepts, particularly AI-powered services, is anticipated to yield immediate advancements in the digitalization of railway transportation, enhancing efficiency and safety measures. [ABSTRACT FROM AUTHOR]

Due to the growing interest in lightweight constructions, the continuous casting of nonferrous metals is continuously developing as a result of the cost-effectiveness of this process, which combines several stages of sheet production. Unfortunately, because of the characteristics of the process, the parameters in the roll gap, such as, for example, pressure and temperature, are unknown, significantly affects the understanding of the phenomena occurring in the material during rolling. Therefore, at IMF Freiberg, a sensor consisting of a piezo sensor and two thermocouples measuring the temperature at two different heights was mounted on the surface of the roll, making it possible to control the process parameters live during the TRC trial. The measurements were further supported by a digital twin in the form of a layer model, combining a viscous and solid region for each layer in a single tool. The computations in this tool are performed offline and the computation time is in the order of seconds, thus much less than that of the finite element method. Because the layer model measures the temperature of the magnesium strip, FEM simulations were used to validate measurements from thermocouples. Experimental results have been obtained that allow for a direct correlation between the development of the pressure and temperature and the length of the fully solidified LD part in the roll gap zone, which correlates directly with the effective total equivalent strain. Using the sensor and layer model, it is possible to train a digital twin that can be used for online estimation of the final strip properties obtained in the TRC process. [ABSTRACT FROM AUTHOR]

In the sintering of advanced ceramics, a digital twin consisted of a finite element model can predict the final shape and microstructure of a sintered ceramic. However, to minimise uncertainties, it is essential to continually update the mechanical properties in the digital twin using data collected from the manufacturing process. One promising approach for achieving this is through artificial neural networks (ANN). This study introduces a machine learning strategy to update the constitutive behaviour of advanced ceramics in finite element analysis of sintering deformation. A major challenge in implementing machine learning in material processing is the huge amount of data required by the training and validation of an ANN, which are often unavailable or incomplete in a real manufacturing process. This study demonstrates that the data requirement can be reduced by employing a two-step training technique. Firstly, the ANN is trained using a nonlinear constitutive law, which describes a general relationship between the strain rates and stresses. Subsequently, the weights and biases of the ANN are transferred for the retraining using limited experimental data for an actual ceramic. It is shown that such approach can successfully capture the nonlinear constitutive behaviour of fine-grained alumina without demanding for large amount of experimental data. A case study is provided, highlighting the feasibility of implementing the ANN in a commercial finite element package, to replace the constitutive law and predict the shrinkage and distortion of a sintering part. In particular, the sintered dumb-bell shape part, simulated using the retrained ANN, showed grain size and relative density that markedly different from those using the nonlinear constitutive law. It is important to note that the proposed methodology is generic and can be used to create ANNs to replace constitutive laws in finite element analysis in digital twins for a wide range of other engineering processes. [ABSTRACT FROM AUTHOR]

The synchronous operation of the digital twin model of nuclear power and the actual unit is an important guarantee for the safe operation of nuclear power. Taking the digital twin model of nuclear power steam system as the research object, the proxy model of steam system was constructed by constructing a five-layer fully connected neural network, and then the intelligent optimization algorithm was used to optimize the parameters of the proxy model to realize the automatic synchronization of the digital twin model with the actual unit, and finally an improvement scheme of the intelligent optimization algorithm was proposed and its application was verified. The results show that the proxy model can reach 99. 99% of the accuracy of the digital twin model, the call speed is increased by 5 times, the optimization results of the intelligent optimization algorithm are better than the manual debugging results, the improved scheme can effectively improve the optimization accuracy, and the automatic synchronization effect is remarkable. [ABSTRACT FROM AUTHOR]

A new method was proposed to address fault diagnosis by applying the digital twin (DT) high-fidelity behavior and the deep learning (DL) data mining capabilities. Subsequently, the proposed fault distribution GAN (FDGAN) was built to map virtual and physical entities for the data from the established test platform. Finally, the MobileViG was employed to validate the model and diagnose faults. The accuracy of the proposed method with training samples of 600 and 800 were 88.4% and 99.5%, respectively. These accuracies surpass those of other methods based on CycleGAN (98.86%), CACGAN (94.92%), ACGAN (86.45%), ML1D-GAN (82.33%), and transfer learning (99.38%). Therefore, with the integration of global connectivity, an innovative network structure, and training methods, FDGAN can effectively address challenges such as network degradation, limited feature extraction in small windows, and insufficient model robustness. [ABSTRACT FROM AUTHOR]