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There currently exists a need for a platform that provides users with the ability to perform extensive, repeatable and meaningful simulation and testing for the hardware and software which compose vehicle/autonomous vehicle systems whilst being broadly accessible, widely supported and provides robust features and development tools. The contemporary implementations of similar systems are either financially exorbitant or highly contained. The system reflected in this paper aims to fill a gap in the industry of vehicle/autonomous vehicle development by extending on currently existing open-source software to provide a highly streamlined platform to support the production of general road vehicle and autonomous vehicle driving systems. The software tools and hardware components chosen for the system will be discussed, followed by the features constructed throughout the development process. The end result of the system is a platform that allows for quick, repeatable, accurate, and nearly endless testing of a digital twin of real life vehicles. This system will allow users to gain valuable simulation and testing data of hardware and software components in a manner which is not always feasible using the traditional methods of autonomous vehicle testing. [ABSTRACT FROM AUTHOR]

Moving a world leading numerical weather prediction system that runs on a dedicated, bespoke, high performance computing cluster and supporting infrastructure, into the heart of a digital twin for climate change adaptation and extreme weather events has been a challenging and exciting journey. In this paper we describe this journey with a focus on those aspects required to leverage the pre-exascale EuroHPC systems that have been made available to Destination Earth (DestinE) to run its computational representation[1]. EuroHPC systems can be effectively used for DestinE and are in fact key assets to deliver the computational power required for Earth system digital twins at global km-scale resolution. At the same time, EuroHPC systems were newly installed and procedures to run them efficiently are evolving. We find that each of these systems is operated by a national hosting entity that implements its own procedures, e.g. for identity and access management, specific system configuration like schedulers, filesystems, software management systems, and specific, sometimes vendor associated, toolchains, tooling, and container runtimes. In particular, the different scheduling policies encountered, required us to adapt our workflows for each site. We found that having dedicated resources available, which was trialed in a period from 16th February to 14th April on LUMI, allowed to achieve high occupation rates, with 92% on the reserved GPU allocation and greater than 97% efficiency on the CPU reservation. Also a stronger focus on federation of these systems, with a focus not only on federation of identities and accounts, but also in the areas of data ownership/transfer, observability, services and service accounts, maintenance coordination and performance portability is required. In general, it should become much easier to transfer a workload, or a digital twin system, from one EuroHPC site to the next and run and maintain them across several sites concurrently. [ABSTRACT FROM AUTHOR]

Industry 4.0 represents a turning point in the thinking of the production model since it is based on digitalized production systems with the aim of improving productivity, product quality, and delivery time to the customer. The digitalization and evolution of information technology allowed the emulation of production system virtual models, namely in the concept of Digital Twin (DT), with the ability to simulate different scenarios providing support for better decision making. This concept not only represents a virtual copy of the physical world that obtains information about the state of the value chain but also illustrates a system capable of changing the development of the production activity according to the fulfillment of the intended business goals. In literature, the concept of the Digital Twin is exhaustively treated as a stand-alone factory (one digital factory represents one physical factory) and underestimates the possibility of a DT oriented to a customized product (a project) that requires decentralized production systems. This paper brings to discussion the relevance of product customized applying DT to smart customization, and the inclusion of decentralized production systems supported by Cloud Manufacturing. [ABSTRACT FROM AUTHOR]

In this research work, a discrete event simulation model (DES) is developed as a digital twin of the supply chain of Antarctic campaigns in a navy ship. This simulation aims to describe the system with all its details and complexity and, through this, support decision-making that identifies a supply chain configuration that significantly improves efficiency based on costs and operating times. For this, the Anylogistix simulation tool generates scenarios and visualizes results through charts and key performance indicators. [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 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]

Many existing manufacturing systems still rely heavily on human workers as the backbone of their production processes. Such systems are commonly termed labor-intensive. Developing Digital Twins for labor-intensive manufacturing lines is a complex and challenging task as human involvement adds another level of uncertainty. While Digital Twins offer numerous benefits, such as improved efficiency, reduced downtime, and enhanced decision-making, they also come with unique challenges when they need to be developed for labor-intensive manufacturing systems. In this paper, we discuss the main challenges and their implications that arise from existing research. Considering these challenges, we propose a framework for developing data-driven Digital Twins of labor-intensive manufacturing systems as an initial step towards addressing these challenges. We illustrate the challenges associated with Digital Twins of labor-intensive manufacturing systems through a practical case study derived from our collaboration with two companies. In the case study, we make necessary preparations for developing Digital Twins for decision support in job scheduling in a hybrid machine-worker environment while considering the well-being of workers. [ABSTRACT FROM AUTHOR]

Data-driven Digital Twin approaches have gained popularity in research literature, particularly in relation to Industry 4.0 achievements. Data-driven Digital Twins process widely available real-time data in an automated manner to extract models from various systems of interest. However, these model extraction approaches commonly lack systematic integration of expert knowledge from human experts, such as engineers. Combining valuable expert knowledge with data-driven model extraction approaches is a highly complex task that can significantly contribute to more accurate models within shorter time periods. In this paper, we provide a comprehensive overview of the research done on fusion of data and expert knowledge for Digital Twins to identify and describe the current gap. Resulting from our findings, we propose an initial framework for integrating expert knowledge and data for Digital Twin model extraction. Subsequently, we provide an overview of the main challenges and opportunities in fusing data and expert knowledge in the context of Digital Twins. [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]

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]

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]

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]

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]

The globalization of products and markets increases the distance between the origin of products and consumers. This leads to a condition where customers don't have information about the origins of their products. Thus, traceability has become an essential sub-system of manufacturing supply chain management. However, due to globalization and complexity of supply chain interactions among the suppliers and manufacturing enterprises, it is hard to pinpoint the exact contributions of different actors in a supply chain. Integrated supply network structure with suitable visibility and usage of real time data transfer is another area of great importance. This paper focuses on how NFT (Non-Fungible Token) coupled with smart contracts could utilize blockchain to make it easier to track the products in a supply chain. Explaining how NFT's could help in tracking the contributions of different stakeholders in a supply chain by tracking the product throughout the entire process of sourcing, production, and sale by using a digital twin. In a manufacturing supply chain enabled by NFT Technology, whenever raw materials are transferred and processed through the supply chain, an NFT would be attached to its digital twin which will capture the created values. Each NFT can easily and uniquely be known by its data stored. Data would be updated based on real time information and will enable the stakeholders to trace the product information about how much each company has contributed to the produced products. The data stored in the form of a smart contract in the blockchain prevents the data being entered from being destroyed, eliminated, or changed without permission. Thus, there is a secure data flow among different stakeholders. [ABSTRACT FROM AUTHOR]

While digital technologies provide the backbone for digital twins of product, process, and performance, early adopters of digitalization recommend that technologies need to be supported by reengineered processes to unlock capacities and empower people. While the existing research literature provides insights into digital technologies, holistic consideration of product development processes and their responses to interventions is found lacking. This study focuses on the experiential inputs of manufacturing professionals to build a perspective of a customer-centric digitalized value chain enriched with digital twins of product, process, and performance. Authors interacted with SME stakeholders, through brainstorming workshops organized under the aegis of a premier Indian industry association. Based on the insights drawn from the expert interactions, monitoring, optimization, integration, and customer satisfaction have been established as the strategic steps of digitalization. This study adds to the digital twin-based engineering knowledge by introducing a versatile process-technology-performance matrix. SMEs can adopt this matrix to tailor solutions to their unique business needs, enabling flexible creation of digital twins. This aspect makes this study relevant and novel for manufacturing SMEs preparing for implementing transformative digital technologies. [ABSTRACT FROM AUTHOR]

Nowadays, the construction industry's digital transformation processes are challenging civil engineering education. In general, students and society underestimate the civil engineering field as an outdated and archaic knowledge area. Despite the defragmentation and heterogeneity of the sector, advances have been made on the path for Construction 4.0 and 5.0 scenarios. From this perspective, the CONSTRUCT-Gequaltec group of the Faculty of Engineering of the University of Porto have been sponsoring research and technological development using hardware and software targeting construction management solutions. This paper presents the work in progress mainly connected to the curricular units regarding project management in the Construction Section of the Civil Engineering Department. It also targets assumptions of some expected results across the research and innovation priorities from ECTP Innovative Built Environment and United Nations Sustainable Development Goals (SDGs). Two fundamental elements of the teaching practices are presented as the Construction Sites of the Future (Laboratory-Based Education initiative) and Digital products catalogue targeting Digital Twins in Construction (Project Based Learning initiative). The learning outcomes address increasing knowledge of innovative solutions, delivering skills to deploy 5.0 actions, and targeting human-centred and sustainable attitudes and values. [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]

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]

Tailings dam failures cause catastrophic impact on the environment and surrounding communities. Incidences of failure in the recent past have caused industrialists and researchers to seek innovative ways for proactively managing their safety and disaster mitigation. Given Industry 4.0 technologies now available, researchers are looking to develop digital tools for cost-effective, realtime monitoring of tailings dams. However, published literature indicates that a reliable framework is still lacking. This paper proposes a framework for developing a data-driven system for monitoring tailings dam stability and early warning detection. The framework relies upon digital twin simulation and machine-learning (ML) techniques, and comprises four main components: realtime data collection, digital twin modelling, ML-based early detection and prediction, and intelligence-driven decision-support. Sensors gather real-time geophysical data from monitored structure, and the digital twin uses this data to simulate dam behaviour. ML algorithms analyse the data and simulations to enable early detection of instability and failure prediction. Literature suggests that digital twin and ML-based approaches may have advantages over traditional monitoring techniques and other AI-based methods. The paper concludes with a discussion of the framework's limitations, opportunities for improvement, and potential for application in mining and geotechnical engineering. The paper serves as a basis for model development and future research. [ABSTRACT FROM AUTHOR]

The advent of new technologies and concepts such as Extended Reality (XR), Cloud Manufacturing, Digital Twin, Industrial Internet of Things is completely changing the manufacturing landscape. Innovative digital tools are available to increase the competitiveness and efficiency of companies. The XR can support the Smart Operator in production, maintenance and training activities. Similarly, Manufacturing as a Service, which is a variation of Cloud Manufacturing, can make corporate architectural systems more flexible and scalable. In this paper, some preliminary results relating to the European 5G-ERA project and the synergistic work that the companies CAL-TEK, TWI and HAL Robotics are conducting is shown. An XR-oriented architecture is proposed, whose usefulness is emphasized by the use of the MaaS concept, and the implementation of a Mixed Reality application is shown, to support an on-site operator. Future developments concern the implementation of a Virtual Reality application for the off-site operator, and the use of the 5G-ERA middleware to exploit the potential of the MaaS paradigm. [ABSTRACT FROM AUTHOR]

The rapid advancement of Industry 4.0 technologies has revolutionized industrial processes and production systems, creating unprecedented optimization opportunities. The Internet of Production (IoP) concept has emerged as a transformative approach that capitalizes on integrating cyber-physical systems, big data analytics, artificial intelligence, and the Industrial Internet of Things (IIoT) to drive efficient, flexible, and intelligent manufacturing. This study explores the current state of expert discourse on the symbiotic relationship between IoP and Industry 4.0, focusing on production technology, automation, and digitalization. For this purpose, we performed a content-centric qualitative review in a systematic manner. The findings show that connectivity, data-driven decision-making, flexibility, intelligent optimization, digital twins, and collaborative networks promotes IoP's potential within Industry 4.0. Overall, this study explains how harnessing the potential of IoP by enhancing efficiency, productivity, and innovation in industrial processes contributes to Industry 4.0, and how understanding the strengths, weaknesses, opportunities, and threats of IoP, leveraging advanced technologies, and data-driven approaches can pave the way for the future of efficient, flexible, and intelligent manufacturing systems. [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]

The human-centered view emphasizes various aspects including usability, acceptance, and understandability, as they emerge in the interaction between humans and intelligent systems. For intelligent assistance systems to provide long-term added value in working environments, it is of major importance to develop a positive user experience. However, a positive user experience does not appear by itself but must be systematically investigated and designed. In this paper, we focus on the role of operator 5.0 in manufacturing and provide an overview of the main challenges and potentials related to working with intelligent systems. Drawing upon state-of-the-art literature and findings from industry (context and expert interviews and user experience workshops), this paper investigates the main discontinuous production processes (i.e., process monitoring, process optimization, process maintenance) that will undergo a series of changes due to integration of cutting-edge technologies (such as Digital Twins, Extended Reality, and Artificial Intelligence). It then identifies requirements and discusses key challenges for the realization of human-centered assistance systems. By highlighting underexplored research questions and potential improvements in industrial settings, this paper provides researchers and practitioners to establish a holistic understanding of the user experience along the industry 5.0 journey [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]

As Industry 4.0 and 5.0 evolve to be highly automated but human-centric, there is a need for process modeling based on digital replicas of physical objects including humans. Knowledge distillation and cognitive cloning offer a way to train operational copies of decision-making black boxes, or donors, without requiring additional data. In this paper, we propose an architecture and analytics for a generative adversarial network, called Cloning-GAN, which enables donor-clone knowledge transfer, including the donor's individual biases. The architecture involves generating challenging samples to be labeled by the donor and used as training data for the clone. We consider several multicriteria requirements for the generated data, including closeness to the decision boundary, uniform distribution in the decision space, maximal confusion for the donor, and challenge for the clone. We present various strategies to balance these conflicting criteria forcing the clone learning quickly the hidden cognitive skills and biases of the donor. [ABSTRACT FROM AUTHOR]

Including disabled workers in value-creating work processes is a fundamental and guaranteed human right and is, therefore, an essential goal of society. In Germany, sheltered workshops create the conditions for this inclusion since they are essential to companies' value chains. A central challenge is the inclusion of disabled workers in the value-creation processes, such as in manufacturing or assembly areas. The skills of disabled workers vary since they have individual impairments. Therefore, this paper presents a digital human model, a Human Digital Twin (HDT), for disabled workers. The model maps their skills and supports the production planning and assembly processes. [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]

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]

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]

In a world whose attention to environmental and health problems is very high, the issue of properly managing nuclear waste is of a primary importance. Information and Communication Technologies have the due to support the definition of the next-generation plants for temporary storage of such wasting materials. This paper investigates on the adoption of one of the most cutting-edge techniques in computer science and engineering, i.e. Digital Twins, with the combination of other modern methods and technologies as Internet of Things, model-based and data-driven approaches. The result is the definition of a methodology able to support the construction of risk-aware facilities for storing nuclear waste. [ABSTRACT FROM AUTHOR]

Manufacturing companies are rapidly moving toward Digitalization. As a result, today's industrial systems integrate information systems and manufacturing systems. In this context, damage caused by cyber-attacks has become apparent, and cyber-attacks on factories are increasing both in Japan and overseas. Therefore, there is an increasing need to implement cyber security measures and education in manufacturing and factories from the BCP (Business Continuity Plan) perspective, which considers safety measures for individual processes and the supply chain. The industrial automation industry is beginning to develop designs through virtual commissioning based on VR product (digital twin) models, and all of this development work is increasingly taking place in virtual space. However, no security educational materials utilize CPS (Cyber-Physical Space), which is free from time and space, based on these technologies. This research aims to develop human resources who can respond to the paradigm shift and realize a suitable environment for cyber security education by constructing a remote training environment using VR product technology that can actually be used in student experiments and practical training. [ABSTRACT FROM AUTHOR]

With growing interest in indoor environment digitization and virtual tour creation, the acquisition of spherical images is gaining importance. Today, these images can come from a wide variety of devices, from dedicated industrial scanners through semi-professional sports cameras to smartphones equipped with the proper software. They also constitute a crucial input for smart indoor systems, such as automatic vacuum cleaners or surveillance cameras, and are essential for the construction of a building's digital twin. One of the most popular data representations used for spherical pictures is an equirectangular panorama (ERP). Although popular, processing it is demanding - ERP is heavily distorted, typically of high resolution, and made with discontinued lighting conditions. Transferring it to the cloud is challenging and the in-place analysis on the edge device is complicated. Nevertheless, ERPs store massive amounts of data, especially in the context of the number of objects captured in a single image. Many techniques dedicated to aberration-resilient object detection were presented. However, they all required proper preprocessing of the data or changed the detection approach so much that it made them unfit for any other task. In this paper, we present research on the processing of an indoor equirectangular panorama (I-ERP) using a family of object detectors called YOLOv5 (You Only Look Once, fifth generation). We discuss the process of adjusting an already available dataset to a newer annotation representation and test the usefulness of these detectors on such input without any changes to the detector models themselves. The results achieved indicate that the newer generation of YOLO detectors can be used with satisfactory results on the panoramic input and that the performance gap in I-ERP processing between dedicated and out-of-the-shelf solutions is closing. [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]

Human-Machine Interfaces (HMIs) are getting more and more important in a hyper-connected society. Traditional HMIs are built considering cognitive features while emotional ones are often neglected, bringing sometimes such interfaces to misuse. As a part of a long run research, oriented to the definition of an HMI engineering approach, this paper concretely proposes a method to build an emotional-aware explicit model of the user starting from the behaviour of the human with a virtual agent. The paper also proposes an instance of this model inference process in voice assistants in an automatic depression context, which can constitute the core phase to realize a Human Digital Twin of a patient. The case study generated a model composed of Fluid Stochastic Petri Net sub-models, achieved after the data analysis by a Support Vector Machine. [ABSTRACT FROM AUTHOR]

The faster and faster IT integration of various categories of activities - from production to administration, passing through medicine, transport, education, government, etc. - led to important changes in terms of obtaining, storing and using data. On the other hand, the increasingly rapid accumulation of societal challenges has exerted immense pressure on the relatively nascent discipline of knowledge management, which is expected to help address increasingly complex issues, primarily related to decision-making aspects that necessitate the selection and transformation of data into information and knowledge. This article focuses on establishing a framework for developing a collaborative, dynamic, heterogeneous decision-making system that includes both human and cybernetic agents as well as digital twins. Such a system creates and sustains a knowledge management flow that is essential for making informed decisions, achieved by selectively collecting and appropriately transforming data into information. [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]

This review aims to define the Metaverse, present the roles of AR, MR and VR, and also the concepts of digital twins and lifelogging. The evolution of applications for Metaverse in various sectors, especially gaming, has created the possibility of using Metaverse for education. We present the vast field of these applications and educational projects. The challenges that educators face are discussed and the potential and limitations of its educational applications are explained. It's suggested to embrace the Metaverse in classes but not in a full-time learning environment, instead, it should be used as a complement, when justified. Some of its limitations may be weaker social connections there are concerns for privacy and security. The big potential offered by Metaverse technologies is the immersive experience of content and social interactions. [ABSTRACT FROM AUTHOR]

Production sequencing methodology using DeepLearning Seq2Seq-LSTM is applied to a tire production case study in Quebec, Canada. Production and demand data are used to predict the most likely product sequences to operate. The comparison of 4 forecasting models, differing in consideration of demand and a statistical component, leads to nearly 70% of good prediction when all machines are studied and 10 production scenarios are considered. This performance reaches 92% for a specific class of machines. The analysis of the forecasts by class of machine allows highlighting 2 factors influencing the performance of the models, namely the ratio of product/machine by class and the total number of available records. The forecasts of possible production scenarios can then be used in a digital twin to evaluate a reasonable number of options and develop a decision support system for production sequencing. [ABSTRACT FROM AUTHOR]

Digital twins are able to bridge the physical and the virtual world, which is especially useful in industrial environments. One small, but rather essential, kind of digital twin is the so called virtual sensor model. This type of model is used to enhance or replace a physical sensor in industrial settings to reduce costs or enable information retrieval from inaccessible locations within a machine or process. The virtual sensor model is usually trained once, whereat only a predefined amount of information without any adaptation possibilities on the clients side is provided. As manufacturers want to provide the possibility of custom adaptations for their clients, the virtual sensor models require to incorporate adaptive features, which also have to handle incomplete data recordings, e.g. uncensored data. Traditional offline machine learning approaches are often insufficient for such adaptive requirements, therefore the usage of online learning approaches is gaining increased attention, to avoid high computational, storage and temporal costs. This paper covers the continued training of such adaptive virtual sensor models, focusing on the handling and integration of censored online data. Different approaches to tackle the problems of catastrophic forgetting in online learning and correction of censored data are presented as well as the handling of censored data in online learning environments. The experiments sections compares various scenarios with and without censored data using an industrial dataset and demonstrates the positive influence of different online learning approaches. [ABSTRACT FROM AUTHOR]

Increased international competition is intensifying pressure on companies such as Atlas Copco to improve the performance of their products; for example, the Compact Bulk Feeder (CBF) element of the Company's 'Self Pierced Riveting Machine'. Digital technologies are advancing rapidly to support the development of new products and processes. This has encouraged Atlas Copco to exploit digitalisation to improve the CBF's functionality and enhance the product's competitiveness in the market. This paper is presenting the results of research of modelling the digital twin of the CBF to enhance its functionalities and operation. A digital twin framework is presented using suitable hardware and software technologies as well as the results of the modelling from physical to the virtual world of two of the CBF functions. [ABSTRACT FROM AUTHOR]

Process Industry is one of the leading sectors of the world economy, characterized however by intense environmental impact, and very high-energy consumption. Despite a traditional low innovation pace in PI, in the recent years a strong push at worldwide level towards the dual objective of improving the efficiency of plants and the quality of products, significantly reducing the consumption of electricity and CO2 emissions has taken momentum. Digital Technologies (namely Smart Embedded Systems, IoT, Data, AI and Edge-to-Cloud Technologies) are enabling drivers for a Twin Digital-Green Transition, as well as foundations for human centric, safe, comfortable and inclusive workplaces. Currently, digital sensors in plants produce a large amount of data, which in most cases constitutes just a potential and not a real value for Process Industry, often locked-in in close proprietary systems and seldomly exploited. Digital technologies, with process modelling-simulation via digital twins, can build a bridge between the physical and the virtual worlds, bringing innovation with great efficiency and drastic reduction of waste. In accordance with the guidelines of Industrie 4.0 this work proposes a modular and scalable Reference Architecture, based on open source software, which can be implemented both in brownfield and greenfield scenarios. The ability to distribute processing between the edge, where the data have been created, and the cloud, where the greatest computational resources are available, facilitates the development of integrated digital solutions with cognitive capabilities. The reference architecture is being validated in the three pilot plants, paving the way to the development of integrated planning solutions, with scheduling and control of the plants, optimizing the efficiency and reliability of the supply chain, and balancing energy efficiency. [ABSTRACT FROM AUTHOR]