Search

The accidental leakage of gaseous hydrogen from hydrogen refueling station facilities has the potential to result in a large fire and explosion catastrophe. The ability to forecast the spatiotemporal evolution of hydrogen plumes in real-time is crucial for monitoring the distribution of plume concentrations and mitigating the risk of fire and explosion. The utilization of deep learning has been extensively employed in a diverse range of spatiotemporal forecasting tasks. However, these approaches encounter limitations in accurately quantifying uncertainty when predicting spatiotemporal concentrations and plume boundaries. This research paper introduces a novel model called DPSTNN_H 2 Evolution, which is a deep probabilistic spatial-temporal neural network designed for forecasting the spatiotemporal evolution of hydrogen plumes. The benchmark dataset is constructed by the implementation of numerical simulations pertaining to the inadvertent leak of hydrogen within a hydrogen refueling station. The findings of the study indicate that incorporating quantified uncertainty information might enhance the precision of plume boundaries and the resilience of plume concentrations when predicting the spatiotemporal development of hydrogen. By employing Monte Carlo sampling with a sample size of m = 100 and a dropout rate of p = 0.1 , the model demonstrates the ability to provide real-time inference for 10 instances of plumes, while maintaining a high level of accuracy with R2 = 0.9829. In comparison to the current state-of-the-art model, the proposed model demonstrates superior accuracy and robustness in forecasting the spatiotemporal evolution of hydrogen plumes. In general, our proposed model has the potential to serve as a dependable option for the development of a digital twin for emergency management of hydrogen refueling stations. • Probabilistic spatial-temporal neural network model for hydrogen plume evolutions is proposed. • Normalized uncertainty contours and uncertainty intervals to improve accuracy and robustness. • Numerical experiment of hydrogen leakage and dispersion in refueling station are conducted. • Optimal hyper-parameters of the proposed model are determined • Comparison between the proposed model with point-estimation based model is conducted [ABSTRACT FROM AUTHOR]

3D scanners have great potential to boost the efficiency of geometric inspection if their traceability is established by identifying measurement bias and uncertainty. This article introduces a digital twin for a structured-light 3D scanner that provides these two essential measures in a real-time manner. The error quantification starts from iterated scans taken from a CMM-calibrated ball-plate artifact at eight different configurations. The differences between the erroneous scan readings of the artifact's distance features and the corresponding calibrated values in the CMM set give a spatial distribution of the scanner's error. Then, three polynomial functions map the Cartesian coordinates of the scan set onto their counterparts from the CMM set. In this error model, a least-square approach determines the polynomial coefficients. Evaluating the error model for a given point cloud returns a vector field of volumetric errors, which is then followed by error corrections and then GD&T calculations. The uncertainty propagation is pursued through the three steps of model development, error map evaluation, and GD&T analysis. The Monte-Carlo estimations benchmark and validate the GUM framework for the uncertainty associated with the outputs of each step. Experimental verifications of the proposed methodology suggest that the error correction improves the mean and the standard deviation of measurement bias by 50% and 27%, respectively. The uncertainty map provides the coverage probabilities of 62%, 88%, and 96% for the coverage factors of 1, 2, and 3, while the expected theoretical levels of confidence are 68%, 95%, and 100%. • The digital twin provides error maps for scan data as a field of error vectors. • Corrections reduced the error average from −10 to −5 μm and error dispersion by 27%. • Ellipsoidal uncertainty regions grant coverage probabilities close to expected levels. • GUM framework reliably estimates the uncertainty of digital twin outputs. • Fast uncertainty analysis allows using digital twin in real-time in-process metrology. [ABSTRACT FROM AUTHOR]

Management of metabolic disorders involves continuous monitoring of physiology. The growing use of glucose sensors, insulin pumps, and other wearable devices, as well as smartphone-based decision support apps, generates large amounts of real-time data, making diabetes a data-rich domain conducive to digital twin implementation. Mechanistic models of glucose–insulin dynamics have been developed in diabetes research, providing a solid foundation for developing digital twin representations of individual patients' metabolic processes. Artificial intelligence (AI) will play a role in improving the accuracy of digital twin technologies and enable their adaptation and personalization. Case studies have demonstrated that personalized approaches that leverage digital twin technology hold promise for optimizing the management of diabetes by tailoring interventions to individual patients' needs and characteristics. Digital twin technology is emerging as a transformative paradigm for personalized medicine in the management of chronic conditions. In this article, we explore the concept and key characteristics of a digital twin and its applications in chronic non-communicable metabolic disease management, with a focus on diabetes case studies. We cover various types of digital twin models, including mechanistic models based on ODEs, data-driven ML algorithms, and hybrid modeling strategies that combine the strengths of both approaches. We present successful case studies demonstrating the potential of digital twins in improving glucose outcomes for individuals with T1D and T2D, and discuss the benefits and challenges of translating digital twin research applications to clinical practice. [ABSTRACT FROM AUTHOR]

With the ever increasing number of active satellites in space, the rising demand for larger formations of small satellites and the commercialization of the space industry (so-called New Space), the realization of manufacturing processes in orbit comes closer to reality. Reducing launch costs and risks, allowing for faster on-demand deployment of individually configured satellites as well as the prospect for possible on-orbit servicing for satellites makes the idea of realizing an in-orbit factory promising. In this paper, we present a novel approach to an in-orbit factory of small satellites covering a digital process twin, AI-based fault detection, and teleoperated robot-control, which are being researched as part of the "AI-enabled Cyber–Physical In-Orbit Factory" project. In addition to the integration of modern automation and Industry 4.0 production approaches, the question of how artificial intelligence (AI) and learning approaches can be used to make the production process more robust, fault-tolerant and autonomous is addressed. This lays the foundation for a later realization of satellite production in space in the form of an in-orbit factory. Central aspect is the development of a robotic AIT (Assembly, Integration and Testing) system where a small satellite could be assembled by a manipulator robot from modular subsystems. Approaches developed to improving this production process with AI include employing neural networks for optical and electrical fault detection of components. Force sensitive measuring and motion training helps to deal with uncertainties and tolerances during assembly. An AI-guided teleoperated control of the robot arm allows for human intervention while a Digital Process Twin represents process data and provides supervision during the whole production process. Approaches and results towards automated satellite production are presented in detail. • An automated production system for modular small satellites is proposed. • Central element is a force-sensitive robotic assembly, integration & testing system. • AI and machine learning approaches are applied to make the process more robust. • A digital process twin supervises the autonomous production. • A multi-modal shared control approach allows human teleoperation if required. [ABSTRACT FROM AUTHOR]

This case study shows how ÖBB-Infrastruktur AG, which provides the Austrian rail infrastructure on behalf of the Republic of Austria, uses a criticality analysis based on an accurate digital representation of the track and route network, including the intersection of different specialist views. This is done to derive objective decision-making bases for asset management to control the availability of routes and assets and the allocation of resources. The calculation of criticality and the classification of assets into modular classes for standardisation are illustrated using the example of the wear-driven 'switch'. [ABSTRACT FROM AUTHOR]

The rapid expansion of industrialization and the increase in energy demand make energy resource management necessary in various industries. Creating digital twins from available streaming data offers a practical way to enhance the efficiency of manufacturing systems. Digital twins allow for detailed analysis of key metrics, such as the energy consumption of complete manufacturing systems. Utilizing digital twins can greatly assist in improving manufacturing processes for reduced energy consumption. In this paper, we investigate the data requirements for the generation of energy-oriented digital twins of manufacturing systems. For this purpose, we developed a case study to illustrate and outline the necessary data requirements for a manufacturing system to enable the collection of data that can be used to extract energy-oriented simulation models. We, subsequently, analyze and generalize our findings that can be applied to a broader context. [ABSTRACT FROM AUTHOR]

Digital Twins allow for designing, operating, and optimizing running systems by replicating the operations of the physical components in a digital environment. Interconnectivity between Digital Twin models and corresponding real-world counterparts facilitates continuous examining of the actual system, resulting in nearly real-time analysis and decision-making support. Validation is an integral part of Digital Twins as underlying models must accurately reflect the corresponding physical systems according to predefined objectives. The near real-time nature of Digital Twins demands a continuous validation process to ensure models' accuracy. Labor-intensive manufacturing, where humans are at the heart of manufacturing processes, is a sector that encompasses a wide range of industries, from toys and apparel to medical devices and automotive components. This sector continues to play a vital role in emerging economies, offering employment opportunities that mitigate poverty and enhance social stability. Enabling Digital Twins for labor-intensive manufacturing systems opens many opportunities towards humancentricity and improvement of well-being of human operators. In these systems, however, human data must also be considered for Digital Twin development and the corresponding validation processes. Handling human data further complicates the creation and validation of Digital Twins. To the best of our knowledge, there has not been a comprehensive study on the validation of Digital Twins in labor-intensive manufacturing. In this paper, we review Digital Twin validation in manufacturing, focusing on systems that feature data from human operations. As a result, we outline the current challenges of validation of Digital Twins in labor-intensive manufacturing environments and suggest future research directions. [ABSTRACT FROM AUTHOR]

This research leverages the potential of digital twin technology to present a novel method of predictive maintenance for 6G communication networks. With the increasing need for 6G networks to be consistently reliable and always available, this essay emphasises the need for more advanced maintenance techniques. The seamless integration of digital twin technology makes it possible to monitor, analyse, and simulate the 6G network in real time. This innovation makes it easier to use artificial intelligence to fine-tune maintenance schedules and anticipate maintenance requirements in advance. The paper fervently promotes the use of digital twin technology as a vital instrument for raising the bar for maintenance requirements in the context of 6G networks. [ABSTRACT FROM AUTHOR]

This paper presents a digital twin-assisted online monitoring and control system for machining operations. Process information including Cutter-Workpiece Engagement (CWE) maps are extracted from the virtual model of the process and fused with the CNC internal data as well as the measured external sensor data. Several applications are illustrated to demonstrate the system. Adaptive control of cutting load based on directly measured forces or reconstructed forces from an accelerometer-integrated tool holder is demonstrated. The spindle current data integrated with CWE information from the virtual model is utilized for the estimation of tool wear progression and tool breakage detection. Digital twin-assisted chatter detection and avoidance are presented by avoiding false alarms at the cutter's entry into and exit from work material which triggers transient vibrations. It is shown that by utilization of the digital twin data and information fusion, robust process monitoring and control can be achieved while anomalies and false alarms are avoided. [ABSTRACT FROM AUTHOR]

In light of the global industrial sector's steadfast pursuit of sustainable solutions to mitigate carbon emissions and efficiently minimize energy inefficiencies, the significance of novel approaches to energy optimization becomes increasingly evident. This research presents a novel digital twin concept that is designed to enhance the efficiency and effectiveness of high-temperature heat pumps in industrial renewable energy systems. Through the utilization of real-time data and complex computer modeling techniques, our proposed digital twin model seamlessly presents a comprehensive perspective on energy flows. This approach identifies inefficiencies and delivers practical insights to mitigate waste. The incorporation of this approach into pre-existing eco-conscious renewable energy systems has the potential to greatly enhance the effectiveness, predictability, and long-term viability of industrial processes. The empirical findings, obtained from multiple case studies conducted in industrial settings, provide evidence of the potential benefits in terms of energy waste reduction, and maximize the durability of systems. The results of our initial studies provide a foundation for the utilization of digital twin technologies in the field of industrial renewable energy systems optimization, representing a significant advancement towards a more environmentally sustainable industrial landscape. [ABSTRACT FROM AUTHOR]

• Cultural heritage is a multi-disciplinary research domain; however data interoperability is really achieved. • Cultural heritage research relies on the integration of data deriving from social sciences and humanities and natural and exact sciences. • A semantic-aware digital twin is offering research opportunities towards the development of large-scale knowledge-bases in cultural heritage, essential for the implementation of the European Commission vision for a European Collaborative Cloud for Cultural Heritage (ECCCH). • Human and machines readable systems, based on a dedicated ontology provide the technological frame for the implementation of the ECCCH.2. The methodologies of Cultural Heritage research and in particular those related to conservation and the analytical study of heritage assets and works of art, recently grouped under the term Heritage Sciences, engage a broad range of disciplines, each with its own characteristic workflows for generating data. Consequently, an emerging challenge is the need to define a digital framework for multi-source data integration, associated with a single heritage asset, but generated by various tools and methods which are often pursued by different research groups and at different times. This digital framework is discussed in this essay as the digital twin of a heritage asset, comprising of the documentation data associated with a heritage asset and its virtual representation. To best describe and define a Heritage Digital Twin ontology and its associated knowledge graph, we use a specific example drawing from art historical and analytical investigation of a 13th century Italian painting masterpiece, the Crucifixion of Christ, by Giovanni Baronzio, one of the leaders of the so-called Rimini School of painting which was greatly influenced by the work of the famous Renaissance master Giotto di Bondone. [ABSTRACT FROM AUTHOR]

A clean solar hydrail, a locomotive operated by solar and hydrogen energy, has received significant attention as an alternative to a current fossil fuel-based locomotive. Nevertheless, the feasibility test of solar hydrail is still challenging since it has not yet been practically developed. Herein, we employed digital twin technology to design and operate solar hydrail and diesel locomotive. 30 years of operation simulation starting from 2021 was conducted based on the data of Gangwon-do, South Korea. It was confirmed that replacing the single diesel locomotive with solar hydrail can provide about 59 % lower net present cost and annual harmful emission reduction of approximately 300 tons. Sensitivity analyses were also conducted to mitigate future uncertainty, and the solar hydrail was a better option than the diesel locomotive in every scenario. This study demonstrates that solar hydrail is the successful substitute for the diesel locomotive currently operated in South Korea. • 30 years of operation was simulated for solar hydrail and diesel locomotive. • Solar hydrail can have $2,026,279 lower NPC than diesel locomotive. • Solar hydrail yearly saves about 300 tons of emissions compared to diesel locomotive. [ABSTRACT FROM AUTHOR]

Accident scenarios leading to full or partial fire engulfment represent a serious threat to tanks used for the transportation and storage of dangerous goods. The present study focuses on the impact of partial engulfing fire scenarios on an LPG tank. An experimentally validated CFD modeling approach was further developed and applied to perform a parametric study by varying the exposure mode and the filling degree. The results of the simulations show that both these factors have a strong influence on the pressurization rate, the energy accumulation, and the high-temperature mechanical weakening of the tank structure. Fire scenarios where the flame zone is at the top or at the end of the tank are particularly critical when the liquid filling degree is low or medium. Liquid thermal stratification is evident in all the case studies analyzed and, in most of them, it strongly affects the vessel pressurization curve. The comparison to the results obtained using a lumped model showed that this provides non-conservative pressurization results for high filling levels (80%). The results show that a validated CFD approach can be used as a virtual workbench to support tank design within a digital twinning approach. [ABSTRACT FROM AUTHOR]

Hydrogen release and dispersion from hydrogen refuelling stations have the potential to cause explosion disaster and bring significant causalities and economic losses to the surroundings. Real-time spatial hydrogen plume concentration prediction is essential for the quick emergency response planning to dissipate such flammable vapor cloud and prevent explosion disaster. Deep learning approaches have recently been applied to real-time gas release and dispersion modeling, however, are 'over-confident' for spatial plume concentration and boundary estimation, which could not support the robust decision-makings. This study proposes a hybrid deep probability learning-based spatial hydrogen plume concentration prediction model, namely DPL_H 2 Plume by integrating deep learning and Variational Bayesian Inference. Numerical model of hydrogen release and dispersion from hydrogen refuelling station is built to construct the benchmark dataset. By using such dataset, two pre-defined parameters, namely Monte Carlo sampling number m = 300 and dropout probability p = 0.1 are determined to ensure the model's tradeoff between inference accuracy and efficiency. Comparison between our proposed model and the state-of-the-art model is also conducted. The results demonstrate that our model exhibits a competitive accuracy of R 2 = 0.97 as well as an inference time 3.32 s. In addition, our model gives the comprehensive estimations including not only spatial hydrogen plume concentration but also its uncertainty. Also, our model provides the more accurate estimation at plume boundary compared to the state-of-the-art model. Overall, our proposed model could provide reliable alternative for constructing a digital twin for emergency management of hydrogen refuelling station. • Deep probabilistic learning-based hydrogen plume prediction model is proposed. • Optimal hyperparameters of variational Bayesian inference are determined. • Model accurately predicts real-time spatial hydrogen plume concentration given scenario-related parameters. • Model predicts additional uncertainty information for comprehensive emergency decision-makings. [ABSTRACT FROM AUTHOR]

Quality control is a critical aspect in today's fast-paced and competitive business landscape. The increasing digital transformation of manufacturing companies allows for the implementation of even proactive quality control strategies. This, however, requires the proper integration and analysis of shop floor data, regarding monitoring, diagnosis, and prognostics. This is to support defect recognition and recuperation, along with potential system reconfiguration based on knowledge extraction and human experience integration. Digital twins, being virtual replicas of physical assets, support real-time monitoring, analysis, and optimization. However, quality-related aspects may not be related to monitored parameters, thus solely data-driven models may not be accurate enough for proactive quality control. In this work, a hybrid digital twin is proposed, where data-driven models are used to finetune the behaviour of the digital twin based on its physics model. A use case concerning an industrial asset and the heat transfer to a steel bar is investigated with the results presented and commented on. [ABSTRACT FROM AUTHOR]

The operation of heavy equipment in the construction industry requires high-skilled workers: they must safely operate the equipment in an efficient manner to avoid project delays or damages, both to equipment and material as to their colleagues and themselves. Operator productivity and safety are therefore critical in the construction industry. The advancements in technologies such as digitization and automation are more and more applied in the industry, leading to more data and knowledge on the construction equipment and environment. This can be leveraged by a digital twin to provide value for the operator. In this work, we present a digital twin architecture applied to an off highway vehicle, which provides real-time feedback to the operator while operating the vehicle. Monitoring the interactions between an off highway vehicle and workers will result in a safer and more productive environment. This work focuses on detection of the environment and monitoring the state of the vehicle. Images of a low cost camera are used to extract information of the vehicle implement as well as the environment. The motion and state of the machine can be realistically visualized in real-time in Unreal, using a 3D CAD (Computer-Aided Design) geometry model, enriched with additional relevant information and color schemes. A modular pub-sub architecture enables the communication between the sensors and camera, the data processing and the visualization of the digital twin. [ABSTRACT FROM AUTHOR]

In today's VUCA-defined age, accelerated by the rapid digital transformation brought on by the COVID-19 pandemic, the business sector needs to be able to adapt to expected and unexpected changes. Consistently improving the product quality and managing the whole product life cycle, starting from the new product development (NPD) process, and the related data, are important aspects of keeping a company competitive in a global landscape. This paper highlights the growing potential of Industry 4.0, particularly the digital thread (DTH) and digital twin (DT), in addressing these challenges. The research revealed existing gaps in clarity and practical guidelines for digital twin prototypes (DTP) and DTH in the existing literature. Moreover, additional issues within product life cycle management (PLM) were stated, regarding frequent data loss and the complexity of product audits in the perspective of a continuous improvement. Therefore, a definition and novel framework for DTP and DTH implementation was proposed, after analyzing and comparing state of the art literature. This framework is additionally based on product audits carried out during the design stage of the product development process using requirements engineering and quality control information added within the suggested framework. Preliminary tests with DTP and DTH commercial off-the-shelves and prototype solutions were carried out with a consumer product. The tests enabled delivering a better understanding of DTPs, their potential, and their implications for decision-making and implementation. [ABSTRACT FROM AUTHOR]

Classic cars hold substantial value in the automotive industry, and the restoration process plays a pivotal role in increasing their worth. Ensuring the certification of these restoration processes is vital, as is keeping owners well-informed about the ongoing procedures taking place in their appreciated vehicles. By monitoring and controlling these restoration activities, the management of classic car workshops can effectively optimize their operations, empower owners with pertinent information, and preserve the authentic nature of these vintage automobiles. This study aims to develop a digital twin system for a classic car bodywork restoration shop workshop. The latter integrates multiple sensor technologies, including location tracking, humidity and temperature sensing, accelerometer monitoring, and smart plugs, to facilitate the identification of ongoing activities of classic car bodywork restoration process instances. By leveraging these sensors, the digital twin system may simulate and control workshop operations more effectively. We perform a systematic rapid review of related work and, based on state-of-the-art practices, we identify existing architectures and software applications used for creating digital twin systems. Then, we propose the architecture for our digital twin system, detailing its functionalities. We aim at contributing to advancing digital twin technology in classic car bodywork restoration, enhancing its authenticity, and fostering improved management practices, and overall experience for classic car owners. [ABSTRACT FROM AUTHOR]

Digital twins and virtual reality are pivotal technologies in the context of Industry 4.0, facilitating the design, simulation, optimization, and remote interaction with production systems. These technologies also present new prospects for developing immersive and hyper-realistic digital factories within the metaverse. This paper aims to enhance collaboration and communication in a 3D virtual world, focusing on shared reality and the metaverse's integration in digital factories. Our main contribution enables users to have full-body immersive interaction with virtual 3D assets and realistic locomotion in a 3D environment, fostering flexibility in collaboration and environment monitoring/control. To achieve this, we propose a systematic methodology for designing and implementing a digital twin-based factory with an IoT infrastructure. By replicating sensors and actuators in digital twins, real-time asset management synchronized with the physical factory is realized, empowering full-body interaction. To enable full-body interaction with virtual equipment, we employ a human motion tracking system. A proof-of-concept case study, developed using Unity3D game engine, Solace PubSub+ event streaming APIs, and Awinda Xsens wearable inertial sensors as the motion tracking system, validates our proposed methodology. The results of the case study demonstrate the successful integration of digital twins, virtual reality, and the metaverse, enabling real-time monitoring and control, full-body interaction, and immersive user experiences in the virtual environment. [ABSTRACT FROM AUTHOR]

Software-Defined Radios are radio communications devices that have been growing and developing on a larger scale in recent years. Communications are intrinsically embedded in our day by day, thus presenting a higher motivation to use software-defined radios due to its attractive cost. However they present technical limitations. This paper addresses this problem, which is the non-linearity behaviour of gain and frequency in the LimeSDR-USB. That is, this equipment is used to produce a FM signal with an associated frequency and gain before being parameterised according to the internal parameters of each software-defined radio. Each software-defined radio presents a value of frequency and gain of its own, which correlates to the generated signals at the output level. To avoid this, machine learning networks were used, in which networks were trained to adapt to the non-linearity of these devices and correct it without the user noticing. This way, the user sets a desired frequency and gain in a signal, at the output of the software-defined radios, and a neural network calculates which values the software-defined radios should be parameterised, thus mitigating the non-linearity behaviour. This paper presents the evaluation of a laboratory prototype based on low-cost commercial software-defined radios equipment, to replace an expensive metrologically calibrated equipment used for radio frequency tests on a new concept of industrial test station, with description of the integration of Digital Twins, with their physical and virtual parts. [ABSTRACT FROM AUTHOR]

As a digital environment of interconnected virtual ecosystems driven by measured and synthesized data, the Metaverse has so far been mostly considered from its gaming perspective that closely aligns with online edutainment. Although it is still in its infancy and more research as well as standardization efforts remain to be done, the Metaverse could provide considerable advantages for smart robotized applications in the industry. Workflow efficiency, collective decision enrichment even for executives, as well as a natural, resilient, and sustainable robotized assistance for the workforce are potential advantages. Hence, the Metaverse could consolidate the connection between Industry 4.0 and Industry 5.0. This paper identifies and puts forward potential advantages of the Metaverse for robotized applications and highlights how these advantages support goals pursued by the Industry 4.0 and Industry 5.0 visions. [ABSTRACT FROM AUTHOR]

Digital twins are employed to monitor, analyze, and predict the behavior of a manufacturing system. In literature, the concept of digital twin is mainly associated with simulation models, and only a few works addressed the integration between physics-based and data driven models. This work extends this definition and describes how different kinds of knowledge can be integrated in a hybrid knowledge based system supporting digital twin. Following the Industry 5.0 paradigm, the hybrid system can improve the human-centricity, sustainability, and resilience of a manufacturing system. The hybrid digital twin is structured as an enterprise information system, which can be connected to the other information systems of the company, such as ERP, MES, and PLM, as well as other platforms of total quality management and total productive maintenance. A discussion on the application of the proposed approach in fusion welding, as an example of a very complex process, is also presented. [ABSTRACT FROM AUTHOR]

This paper explores the qualification of a digital twin (DT) for a mobile manipulator (MOMA) in industrial applications. We discuss the development of different DT models based on various industrial needs and highlight the dependence of model accuracy on online sensor precision. Limitations of DTs for MOMA are examined, including challenges in respecting qualifiers due to the inability to incorporate unstructured aspects of the factory environment. Through a case study and some examples, we show the latent potential and limitations of DTs for MOMA in industrial contexts. The challenges of fidelity, real-time operation, and environment modeling are discussed. It is emphasized that creating a true digital twin of a mobile manipulator is hindered by the inability to include the complete surrounding environment. Recommendations for future research focus on addressing these limitations to enhance the effectiveness of DTs for MOMA in Industry 4.0 and smart manufacturing. [ABSTRACT FROM AUTHOR]

Industrial collaborative robots play an essential role in smart manufacturing because they improve productivity while also ensuring workplace safety. However, the development of prognostic and health management systems to ensure the reliability of these robots has been a major challenge due to the lack of fault data. This paper proposed a digital twin scheme based on the fusion of the robot kinematic and dynamic models' information down to the powertrains (i.e., the joints motor, and gear) along with the control algorithms and uncertainty accommodation based upon deep learning. The presented digital twin concept has the potential to propel simulation-based fault prediction. We also highlight and discuss challenges and opportunities around the development of the hybrid digital twin for condition monitoring of industrial collaborative robots. [ABSTRACT FROM AUTHOR]

The forthcoming industrial requirements to ensure increasingly flexible and agile production processes will require an ever-increasing ability to recognise, predict and optimise the state of processes with respect to the planned state. Advanced tools such as Digital Twins (DT) and Cyber Physical Systems (CPS) can help manage and address these problems. This paper presents some results of the authors' research on the adoption of a basic methodology for the design and implementation of Digital Twins aimed at manufacturing applications and subsequent integration into a Cyber Physical Production Systems (CPPS). Reference publications were selected based on the level of detail of DT and CPS architectures. The aim is also to combine methodological development with industrial tools and requirements. The architecture is potentially applicable to different production scenarios. Finally, the first results of application on a scale production line are shown. [ABSTRACT FROM AUTHOR]

The use of digital twins in the development and management of stone-cutting devices presents real challenges in the field of industrial stone manufacturing. Digital twins help manufacturers achieve increased precision, efficiency, and cost-effectiveness, ultimately transforming the landscape of stone manufacturing operations. Such digital twins enable real-time monitoring, predictive maintenance, and virtual simulations. This paper explores two options for connecting a physical stone-cutting machine in a company to its digital twin. The first option involves using Industrial Internet of Things (IIoT) devices and a Message Queuing Telemetry Transport (MQTT) broker to enable real-time data exchange. This approach provides agile and efficient communication between the physical machine and its digital twin. The second option employs conventional automation devices and a VPN connection to ensure data security during transmission. The two options were evaluated by considering criteria such as communication speed, security, scalability, ease of implementation, cost, complexity, and technical requirements. The results of the comparative analysis provide valuable insights for the implementation of the digital twin of the stone-cutting machine, allowing the selection of the most suitable option based on these considerations. [ABSTRACT FROM AUTHOR]

Flexibility is often cited as a desirable key characteristic of modern production systems. In ultra-flexible production, machinery and layouts are in a constant state of adaptation to accommodate changing orders, varying products, or evolving conditions. Cyber-physical integration has been proposed as a potential approach to increasing system flexibility with Cyber-Physical Production Systems (CPPS) and Digital Twins (DT) as central concepts. While numerous architectures, frameworks and approaches have been proposed for CPPS and DT development, further research is motivated regarding the development of a requirement-based framework that links together the high-level system property of flexibility and lower-level system components, enabling the analysis, prescription and comparison of systems. Such a framework could enable manufacturers to continuously evaluate and improve manufacturing systems' flexibility as well as make informed design decisions. Ultimately enhancing system flexibility and responsiveness to changing production conditions. This study aims to initiate the development and formulation of such a requirements-based framework linking flexibility and lower-level system components. Additionally, it seeks to introduce the concept of a "cyber-physical continuum," which the study aims to define as a potential quantifiable indicator reflecting flexibility within production systems. This is achieved by leveraging prior CPPS research based on high-level system requirements. These requirements were expanded by branching each requirement into lower-level components creating a more granular scope and providing a finer lens for analysis and assessment. The framework was then applied to assess a high-mix, low-volume manufacturing scenario. Application of the preliminary framework in the case study indicates its potential utility in providing a useful view of the cyber-physical content of a system. Moreover, it serves as a valuable guide for pinpointing areas for improvement and development. By developing a framework that seamlessly links high-level flexibility requirements with detailed implementation requirements, systems can be comprehensively evaluated, methodically prescribed, and effectively compared. As future work, further refinement and validation of this framework will be crucial to ensuring its validity and applicability across diverse manufacturing contexts. [ABSTRACT FROM AUTHOR]

This paper focuses on the implementation of predictive maintenance processes using both physics-based and data-driven methods, specifically applied to a Salvagnini panel bender. The primary objective is to enhance production data analysis through digital twin predictions, enabling the calculation of process parameters that cannot be directly measured. Two key aspects are addressed in this study: Firstly, two global measures of the effective cumulative load on a machine during its lifetime are formulated. These measures can be used as criteria for estimating the health condition of a machine and for comparing different machines. To achieve this, dimensionless parameters are derived from actual production data. Secondly, a remaining useful life (RUL) model is developed for two machine components relying on production data analysis and digital twin predictions. The first component under consideration is a hybrid hydraulic actuator. Internal leakage prediction for this component is performed based on its data history, resulting in a corresponding trend curve. Additionally, wear of critical bearings is calculated using an analytical model that depends on the production history log. Through the proposed hybrid approach, this paper aims to enhance the predictive maintenance process by leveraging both physics-based and data-driven methodologies. The findings from this study can offer valuable insights for optimizing maintenance strategies and improving the overall efficiency of similar manufacturing systems. [ABSTRACT FROM AUTHOR]

Ever-changing requirements, conditions, and environments call for a change in manufacturing. Hence, research has increased on new forms of manufacturing, such as distributed manufacturing systems. However, distributed manufacturing systems require an IT architecture that covers real-time monitoring, management, and analysis of geographically distributed production sites. Digital twins allow for such a systematic data control and shopfloor management. They enable complex distributed manufacturing systems to be more autonomous and to be connected, adding value to the production system. Even though there have been research activities and publications on this topic, a systematic review of the current state of specific technologies used for distributed manufacturing is yet missing. This paper reviews and categorizes publications to identify relevant technologies for building digital twins supporting the distributed manufacturing paradigm and objectives of digital twins for distributed manufacturing. [ABSTRACT FROM AUTHOR]

In recent years, supervised deep learning-based methods have achieved significant results in fuel cell system fault diagnosis. However, most existing deep learning-based fault prediction methods suffer from missing fault labels and data limitations due to the difficulty in obtaining fault and degradation data in real fuel cell systems. To address the above challenges, this paper proposes a fault diagnosis method based on digital twin and unsupervised domain adaptive learning. The method has two key features: First, a maximum-relevance minimum-redundancy algorithm is used to select the input signals. Then a high-order fuel cell system model is developed to obtain a large amount of digital domain fault data at low cost by simulating fault injection. Second, domain-invariant features are extracted using a domain-adaptive adversarial learning approach to reduce the distribution differences between the digital and real domains. The method successfully diagnosed nine typical faults in the fuel cell air, hydrogen, and thermal subsystems without real data fault labels. Under dynamic load conditions, the diagnostic accuracy reached 92.5 %. In addition, the method achieves a diagnostic accuracy of over 90 % under domain adversarial training using only normal real data. The experimental results show that the proposed method can achieve fuel cell system fault diagnosis without fault labels and significantly reduce the dependence on fault data. [Display omitted] • An unsupervised fault diagnosis method for PEMFC systems is proposed. • The diagnostic algorithm is investigated for nine typical faults. • The training process of diagnostic framework does not require fault labels. • The algorithm still has high accuracy using only normal test data for training. [ABSTRACT FROM AUTHOR]

A predictive digital twin (DT)-driven dynamic error control approach is presented for accuracy control in high-frequency slow-tool-servo ultraprecision diamond turning processes. An explainable artificial intelligence-enabled real-time DT of the total dynamic error (inside and outside the servo loop) was established using in-line acceleration input data near the tool. A feedforward controller was used to mitigate the total dynamic errors before they came into effect. The machining trials using this approach showed that significant improvement in machining accuracy (87%, surface form accuracy; 95%, phase accuracy with precisions of 0.06 µm and 0.05°), and efficiency (8 times the state-of-the-art) were successfully achieved. [ABSTRACT FROM AUTHOR]

In this work, a c lassless over sampling t echnique, Covert , was developed to improve historical datasets from industrial processing plants to aid process modelling. Using kernel density estimation and nearest neighbour algorithms, sparse regions are identified and resampled, developing a more balanced dataset. When applied to a real dataset from a geothermal power plant, Covert outperforms current best practice (Smote) in uniformly populating the input feature space and generating credible data in the output variable. When used to develop a data-driven model Covert improved model accuracy by 20% when predicting outside the original data's feature space. Smote , however, reduced model accuracy by 6% in the same feature space. Developing reliable models of industrial processes continues to be a significant hurdle in developing a digital twin. Using Covert , existing imbalanced historical data can be used to extend the range of applicability of any process model. [Display omitted] • Development of a new classless data resampling algorithm. • Enriching historical process datasets for process modelling. • Improving extrapolation of process models by up to 20% using data augmentation. [ABSTRACT FROM AUTHOR]

• This review gives panorama of heritage digitization in China and categorizes digital twin techniques into six levels to address three specific tasks. • Terrestrial LiDAR and VR are the most prevalent digital twin technologies for decades, while sensor-based monitoring is recently prompted by policy incentives and legislations. • Most digital twin projects are dominated by public sectors, i.e., cultural management authorities, universities, and research institutes. • Efficient data utilization and the seamless integration of diverse data sources within cross-disciplinary collaborations are still challenging. Recent decades have witnessed digital twin as a toolkit to collect, process, and utilize graphical, spatial and other attribute data of cultural heritage. It has been widely applied on cultural world heritage sites in China, which have significant differences in ages, types, materials, and history of maintenance and conservation. Although specific cases are reported previously, a comprehensive overview is still lacking. This paper explores the association between digital twin and heritage conservation, classifying digital twin techniques into six levels to tackle three tasks: data acquisition, visualization and application. It systematically reviews Chinese heritage digitization practices, considering variations in complexity and purposes, and demonstrates typological and chronological characteristics as well as the composition of stakeholders. [Display omitted] [ABSTRACT FROM AUTHOR]

In recent years, the hype around Digital Twins (DTs) has been exponentially increasing in both industry and academia. DTs are a potential solution to increase automation and advance towards Smart Manufacturing. Manufacturing DTs have been implemented at different hierarchical levels, ranging from system of systems to unit level. Increasing computational capacity and data exchange rates can enable DT implementations for real-time applications. Several literature reviews on manufacturing DTs have been published. However, no previous paper focuses on manufacturing DTs at the unit level for which real-time control is most applicable. Simultaneously, the challenges to engineer DTs with real-time capabilities are enormous, both from a scientific and technological perspective. Therefore, we focus on DTs of single production units such as traditional machine tools, additive manufacturing machines and advanced robotic applications. In this systematic literature review, 96 papers about practical unit level DT applications found in the Scopus database using a combination of the keywords "Digital Twin", "Production" and "Manufacturing" are reviewed. We summarize how DTs are currently implemented and operated, and what potential benefits DTs offer at the unit process level in four categories: generic reference models, services, DT content (models and data) and DT deployment (hardware and software). Following the thematic analysis, an overall discussion, summary of key contributions and identified research gaps, and outlook into future research avenues is given. Key findings of this review can be summarized as: focus on DT components versus being holistic; need to share data and models across multiple stakeholders; lack of physical fidelity of the models; stark contrast of lab scale developments and real world testing, e.g., historical data and storage related challenges; lack of clear definition of DT in industry, and missing semantic interoperability between a wide variety of domains. [Display omitted] [ABSTRACT FROM AUTHOR]

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is characterized by fibrofatty replacement of primarily the right ventricular myocardium, a substrate for life-threatening ventricular arrhythmias (VAs). Repeated cardiac imaging of at-risk relatives is important for early disease detection. However, it is not known whether screening should be age-tailored. The goal of this study was to assess the need for age-tailoring of follow-up protocols in early ARVC by evaluating myocardial disease progression in different age groups. We divided patients with early-stage ARVC and genotype-positive relatives without overt structural disease and VA at first evaluation into 3 groups: age <30 years, 30 to 50 years, and ≥50 years. Longitudinal biventricular deformation characteristics were used to monitor disease progression. To link deformation abnormalities to underlying myocardial disease substrates, Digital Twins were created using an imaging-based computational modeling framework. We included 313 echocardiographic assessments from 82 subjects (57% female, age 39 ± 17 years, 10% probands) during 6.7 ± 3.3 years of follow-up. Left ventricular global longitudinal strain slightly deteriorated similarly in all age groups (0.1%-point per year [95% CI: 0.05-0.15]). Disease progression in all age groups was more pronounced in the right ventricular lateral wall, expressed by worsening in longitudinal strain (0.6%-point per year [95% CI: 0.46-0.70]) and local differences in myocardial contractility, compliance, and activation delay in the Digital Twin. Six patients experienced VA during follow-up. Disease progression was similar in all age groups, and sustained VA also occurred in patients aged >50 years without overt ARVC phenotype at first evaluation. Unlike recommended by current guidelines, our study suggests that follow-up of ARVC patients and relatives should not stop at older age. [Display omitted] [ABSTRACT FROM AUTHOR]

Single-cell protein (SCP) is an evolving biotechnological concept that can potentially align protein production with the global sustainability commitment. This review presents an overview of SCP's current outlook, including production, commercial prospects, and sustainability status. It also elaborates on the potential of the evolving digital twin concept in improving SCP production's efficiency and sustainability performance. An expanding body of work was identified, with well-explored fermentative approaches and varying substrates and microbes. Whereas interest in first-generation substrates such as methane is gradually fading due to their high competitiveness and acquisition cost, second-generation substrates such as lignocellulosic materials and agro-industrial wastes are rapidly evolving due to their availability, low cost, appreciable nutrient density, and alignment with the circular bioeconomy path. Sustainability assessment of current production attaches substantial environmental, mainly global warming and land use offset, and economic savings to SCP production. Moreover, it emphasizes conventional energy use as a hotspot contributor to all impact categories. However, research on life cycle costing, social life cycle assessment, and environmental nutrition concepts is limited. Current trends project rapid market growth for SCP due to expanding feed, food, and nonfood applications. The rapid influx of transformative innovations such as mixed culture biotechnology and the emerging digital twin concept that present catalytic advantages in achieving market growth and sustainability cobenefits are backing these projections. Several sensor and predictive technologies are available to enable an SCP-digital twin path, presenting an opportunity to enhance green and precision SCP production. Despite these innovations, significant efforts are required to overcome limitations concerning toxicity, legislative restrictions, technical constraints, and consumer neophobia to bolster commerce and market value. [Display omitted] • Agro-industrial waste upcycling should frontline circular bioeconomy actions for value recovery. • Single Cell Protein production can align protein supply with global sustainability commitment. • Closing sustainability loop in pulse protein extraction with single cell protein pathway. • Combine environment, economic, social, and nutrition metrics in single cell protein assessment. • Adopt digital twin to for better control and optimization of single cell protein production. [ABSTRACT FROM AUTHOR]

The digital world has experienced extraordinary growth during the past few years. The digital world has infused every area of our lives, and this shift has been nothing short of astonishing. The idea of the "Digital Twin" (DT) is at the forefront of this digital revolution. It's a brand-new, extremely valuable asset that will have long-lasting effects. The DT will be examined in this study, along with the technology that makes it feasible. DTs may be beneficial in the industrial sector, healthcare, and agriculture. The interaction of contemporary technologies like the Internet of Things (IoT), cloud storage, and artificial intelligence (AI) is the basis of DTs. DTs strength and potential are derived from these technological foundations. They provide DTs with unmatched accuracy and efficiency for simulating, monitoring, and enhancing physical systems. We must acknowledge, though, that the combination of these technologies presents serious security issues. The difficult problem of security needs to be resolved in order for DTs to reach their full potential. The highest degree of authenticity, privacy, and accessibility must be used to protect the virtual self. In DT technology, security covers a wide range of issues, from protected data storage to impenetrable communication channels. Secure essential infrastructure, private data, and proactive monitoring technologies are necessary to create a more robust digital environment. Maintaining the safety of these components is essential for DT systems to continue functioning securely and dependably, in addition to removing any hazards. [ABSTRACT FROM AUTHOR]

With the improvement of digitalization and the development of information technology, intelligent campus management has become an important research direction in the current education field. Based on digital twin technology, this article proposed a design scheme for a smart campus management platform to address the current problems in campus management. This could manage and serve students, teachers, parents, and other aspects, and achieve the mining and analysis of school big data. Through the implementation and application of the platform, the efficiency and accuracy of campus management could be improved, and the modernization of education could be further promoted. The smart campus management platform of digital twin utilized digital twin technology to digitize the campus environment, equipment, students, teaching resources, and other information of the school, thus achieving three-dimensional visualization, intelligent analysis, and feedback. The experimental results showed that the security of the smart campus management platform designed in this article exceeded 90% and performed well in system testing. [ABSTRACT FROM AUTHOR]

Decarbonization will allow the automotive sector to achieve net-zero greenhouse gases emissions. Electric and hydrogen vehicles will contribute to that final target. Fuel Cell Hybrid Electric Vehicles FCHEVs aim at solving some of the issues of the otherwise standalone options: higher range in comparison with Battery Electric Vehicles, and allowing the fuel cell to be downsized, operated at greater efficiency, and less exposed to degradation. In this paper, a FCHEV Digital Twin DT is proposed to characterize the vehicle behavior during the WLTP driving cycle with accurate modeling of the auxiliary systems. Two control strategies, Range Extender and optimized Fuzzy logic control, are compared to show the impact on the vehicle and components performance and highlight the impact, energy-wise, of such systems in the Balance of Plant. Increased range by nearly 30 km is demonstrated while simultaneously limiting load stresses on the battery pack. • Detailed modeling of Fuel Cell Hybrid Electric Vehicle (powertrain and auxiliaries). • Vehicle auxiliaries account for almost 35–45% of the total energy consumption. • Ambient temperature strongly impacts on vehicle range (up to 13% of reduction). • HVAC is the most impacting auxiliary subsystem from energy standpoint. • Optimized fuzzy logic control increases range up to 4.5% respect to Range Extender. [ABSTRACT FROM AUTHOR]

Air pollution is a pressing issue in cities, and managing air quality poses a challenge for urban designers and decision-makers. This study proposes a Digital Twin (DT) Smart City integrated with Mixed Reality technology to enhance visualization and collaboration for addressing urban air pollution. The research adopts an applied research approach, with a focus on developing a DT framework. A use case of DT development for Jakarta, the capital of Indonesia, is presented. By integrating air quality data, meteorological information, traffic patterns, and urban infrastructure data, the DT provides a comprehensive understanding of air pollution dynamics. The visualization capabilities of the DT, utilizing Mixed Reality technology, facilitate effective decision-making and the identification of strategies for managing air quality. However, further research is needed to address data management challenges to build a DT for Smart City at scale. [ABSTRACT FROM AUTHOR]

Sustainable city development is an approach to city planning and management that takes into account the balance between economic, social and environmental aspects in order to ensure sustainable social development, environmental protection and ensuring a high quality of life for residents. The aim of the article is to draw attention to the problems in the field of parking vehicles in a smart city, which should be solved as soon as possible. Problems related to parking in the city are a common challenge that can affect the comfort of living of residents, traffic, natural environment and transport efficiency. Many cities lack parking spaces in relation to the number of vehicles, leading to difficulties in finding a parking space. This can lead to traffic jams that increase the time it takes to find a parking space, increase traffic-related emissions and reduce the quality of life for residents. The research method to solve the problem related to the management of parking spaces is focused on the analysis of the city's problems and guidelines resulting from the sustainable development of the European Union. In the application part, open data resources were used to carry out the simulation in the proprietary application solution. The result of the article, which is a scientific research, is the presentation of the author's application in the Digital Twin concept for the simulation of parking space management. The simulation is the result of research conducted using open data published on the Internet. The conclusion of the research is that in the process of effective management of parking spaces, simulation in the Digital Twin concept can be used. There is great potential in this approach. [ABSTRACT FROM AUTHOR]

Different studies highlighted the environmental impacts of geothermal power plants (GPPs), especially in the operational phase. Primary data are essential for reliable environmental assessments, but these are not available when considering alternative configurations. Rigorous simulation can offer such a degree of information, representing also a tool for a digital twin technology transition. Considering a GPP in Tuscany, Italy, four alternatives configurations are simulated in UniSim Design®, performing accurate refinements to the thermodynamic model to properly estimate the behavior of different pollutants (Hg, H 2 S, NH 3 , and SO 2) among the various unit operations. The configurations alternate direct-contact and surface condensers as well as wet and dry towers as cooling systems, while a fifth one adopts an organic Rankine cycle. The actual plant model has been reconciliated with data collected during two different monitoring campaigns. Performance analysis shows a trade-off: the lowest pollutant emissions are obtained using the dry tower generating less net energy (20.17 MWe and 20.83 MWe), instead, adopting surface condenser and wet tower, 22.26 MWe are produced with NH 3 emissions almost 3 times the measured ones. The CO 2 and CH 4 amount in the well fluid is not abated in any configurations and therefore their emissions are the same among them. • Operational environmental assessments of geothermal power plants need primary data. • Modeling a geothermal power plant with UniSim Design® and custom thermodynamic package. • Data reconciliation with a power plant in Italy under different seasonal conditions. • Use of direct-contact or surface condensers and wet or dry towers for cooling or ORC. • Net power production and pollutants emissions compared for the various plant set-ups. [ABSTRACT FROM AUTHOR]

Roaster furnace is a large-scale equipment in zinc smelting process, which plays an significant role in safety production and enviromental protection. An accurate digital twin of roaster furnace can help to explore the influence of control parameters and serve as a test platform to verify the effectiveness of control strategies. However, the traditional thermodynamics cannot be used for dynamic modeling, and inability to measure key data greatly affects the accuracy of kinetic modeling. Therefore, this paper establishes a novel digital twin of zinc roaster furnace based on knowledge-guided variable-mass thermodynamics. First, based on the integration of mechanism analysis for mass balance and energy balance, a dynamic modeling method is proposed. Then, particle swarm optimization (PSO) algorithm is introduced to optimize the parameters of conversion rates guided by knowledge. Finally, by connecting the dynamic model with distributed control system (DCS) through OPC communication protocol, a digital twin of roaster furnace is constructed. Extensive experiments show that the simulation results of the digital twin roughly agree with the actual industrial data under steady and dynamic working conditions, and the application of the digital twin on the performance analysis of control parameters and testing of control strategies can provide guidance for the optimal control of roaster furnace. [ABSTRACT FROM AUTHOR]

In the wind energy industry, a cost-benefit digital twin (DT) will be very useful for managing the operation of a wind turbine. For instance, a DT could provide information on structural health conditions in real-time as well as projections for the near future. In this work, we employ a component-based Reduced-Order Modelling (ROM) technique to construct a DT of a parameter-varying offshore wind turbine system on a monopile. The DT consists of a ROM model for modal analysis and structural response prediction under wind and wave loadings. First, an offline library is pre-computed, which contains a series of component archetypes. Then, the component-based ROM is assembled out of these archetypes, such as the parts in the blade, hub, nacelle, and tower. With the computation speed of two orders (approximately 650x) faster than a Finite Element Analysis (FEA) model and high accuracy (less than 0.2% error), the DT could be able to provide almost instant predictions of the modes and the responses of the turbine structure due to the wind-wave loading as well as projections of structural health conditions. [ABSTRACT FROM AUTHOR]

Collecting and utilizing data in industrial production are becoming increasingly important. One promising approach to utilize data is the concept of digital twin (DT). DTs are virtual representations of physical assets, updated by real data and enhanced by models. This paper provides an overview of DTs for cutting processes. After giving a definition, we discuss requirements derived from representative use cases. As process models are central for DT creation, we present an overview of the latest research as well as conditions for how it can be implemented in industrial environments. The paper concludes with main challenges for future research. [ABSTRACT FROM AUTHOR]

Manufacturing processes are becoming increasingly data-driven. Integrating manufacturing data and process models in real-time, a digital twin (DT) may function as an autonomous and dynamic digital replica. This, in turn, may enable manufacturers to not only understand and monitor a process but also proactively control it in real-time or a product over its life cycle. This paper examines the DT concept and its evolution and presents a future DT framework. DTs' key components (e.g., process models) and implementation are focused on additive manufacturing, electrical discharge machining, and electrochemical machining. Furthermore, current challenges and future research directions are summarized. [ABSTRACT FROM AUTHOR]

• Present non-destructive testing methods, fault diagnoses, maintenance, and digital twin used as damage detection techniques and structural condition monitoring techniques are addressed and evaluated. • Material perspectives on advanced functional materials for damage detection are explored and evaluated. • The further trend of condition monitoring of wind turbine blades is considered thoroughly following maintenance, fault diagnoses, and digital twin. In recent decades, renewable energy has attracted attention as a viable energy supply. Among renewable energy sources, offshore wind energy has been considerably growing since longer and larger wind turbine composite blades were deployed. The manufacture of the longer and larger composite blades leads to more wind energy production. However, the wind turbine composite blades are susceptible to damage and defects due to multiple structural loads and harsh operating environments in service. Hence, condition monitoring and maintenance of wind turbine composite blades require in-depth investigation to prevent structural damage and defects and to improve remaining lifetime of the composite structure. The types of damage and defects in wind turbine composite blades are typically delamination, debonding, and cracks, which are influenced by the intrinsic structural nonlinearities, manufacturing process stage, and harsh environmental impacts in service. For these reasons, the regular condition monitoring of the composite blades is required to assess degradation in performance and structural condition to minimise levelised energy costs for maintenance. To improve reliability and sustainability, data-driven inspection with digital twin technology is reviewed as a trend of condition monitoring frameworks. Advanced functional materials to potentially assist current non-destructive testing (NDT) methods or to be utilised as self-sensing performance are reviewed. From manufacturing to the system level, a comprehensive review on progress and trends of monitoring of wind turbine composite blades is carried out including physics-based NDT methods, data fusion in sensor networks, automated system, mechanics, and digital twin technology with the environmental coupling. [ABSTRACT FROM AUTHOR]

The framework of the Geometrical Product Specification defines the nominal geometry of the components and the characteristic tolerances. Various ISO standards describe the nine defined geometrical properties per characteristic of a workpiece. To prove conformance to the task specific tolerances according ISO 14253–1, the uncertainty budget needs to be determined. This paper presents a digital twin of an optical measurement system as a holistic concept to perform the task specific uncertainty determination. Different measurement strategies are applied to illustrate their effect on the measurement uncertainty of geometrical properties. Not all geometrical properties are relevant for an optical measurement system. Therefore, the discussed properties as a function of the optical measurement strategy in this paper are: size, distance, form and orientation. • Presentation of a digital twin of an optical measurement system. • Discussion of relevant ISO standards. • Evaluation of the scan strategies on the measurement uncertainty. [ABSTRACT FROM AUTHOR]

Due to the high cost of their warehousing operations, many manufacturing companies are looking to increase the efficiency of their warehouse. To this end, Industry 4.0 technologies offer several solutions to address this issue. One of them is the digital twin (DT) technology which is relatively new. This paper presents a literature review on DTs in warehousing. The analysis framework used to classify the papers includes the general description of the DT, the possible uses, and the required data. Our results show that several limitations remain to exploit the full potential of a DT. The limitations mainly concern the characterization of the data required. [ABSTRACT FROM AUTHOR]