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Occupational safety and health is among the most challenging issues in many industrial workplaces, in that various factors can cause occupational illness and injury. Robotics, automation, and other state-of-the-art technologies represent risks that can cause further injuries and accidents. However, the tools currently used to assess risks in workplaces require manual work and are highly subjective. These tools include checklists and work assessments conducted by experts. Modern Industry 4.0 technologies such as a digital twin, a computerized representation in the digital world of a physical asset in the real world, can be used to provide a safe and healthy work environment to human workers and can reduce occupational injuries and accidents. These digital twins should be designed to collect, process, and analyze data about human workers. The problem is that building a human-focused digital twin is quite challenging and requires the integration of various modern hardware and software components. This paper aims to provide a brief survey of recent research papers on digital twins, focusing on occupational safety and health applications, which is considered an emerging research area. The authors focus on enabling technologies for human data acquisition and human representation in a virtual environment, on data processing procedures, and on the objectives of such applications. Additionally, this paper discusses the limitations of existing studies and proposes future research directions. [ABSTRACT FROM AUTHOR]

Researchers believe that Industry 4.0 is the new industrial revolution. In this case, we highlight the critical role of decision support tools, such as Simulation and Digital Twins (DT) models, that might help decision-making in strategic decisions in the industry. Additionally, Green Supply Chain is a theme that has been widespread due to all the issues involving pollution and tax breaks that some government offers to industries. Furthermore, enterprises worldwide are becoming more aware of environmental issues. In this subject, an area that has been emerging but is not significantly addressed in the literature is Reverse Logistics (RL). Thus, this work aims to analyse how Simulation and DT are helping RL in the Industry 4.0 context, answering some questions that may support future researchers to begin their studies in the area. [ABSTRACT FROM AUTHOR]

With the advent of the big data era and the rise of Industrial Revolution 4.0, digital twins (DTs) have gained significant attention in various industries. DTs offer the opportunity to combine the physical and digital worlds and aid the digital transformation of the civil engineering industry. In this paper, 605 documents obtained from the search were first analysed using CiteSpace for literature visualisation, and an author co-occurrence network, a keyword co-occurrence network, and a keyword clustering set were obtained. Next, through a literature review of 86 papers, this paper summarises the current status of DT application in civil engineering based on a review of the origins, concepts, and implementation techniques of DTs, and it introduces the application of DTs in the full project lifecycle. This study shows that DTs have great potential to address many of the challenges faced by civil engineering. In this regard, the paper also presents some thoughts on the future directions of DT research. [ABSTRACT FROM AUTHOR]

Digital Twins (DTs) hold significant promise in addressing the challenges faced by food supply chains (FSCs). This paper aims to provide critical insights into the potential for Digital Twins to meet the key challenges of the FSC and establish a comprehensive conceptual framework for their implementation. Following a systematic literature review (SLR), the study identified 81 peer-reviewed, high-quality papers published over the last decade (2012–2023). The typology-driven thematic analysis emphasises the emergent nature of DTs within FSCs, highlighting their key characteristics including monitoring, real-time simulation, and scenario analysis. The identified characteristics, applications, implementation drivers and barriers of Digital Twin form the basis for a novel conceptual framework for implementing DTs in FSCs. Leveraging insights from Innovation Adoption Theory and the Technology-Organization-Environment (TOE) framework, the study outlines a structured five step implementation process divided into three stages. Notably, technology assessment and performance evaluation emerge as two innovative steps necessary for the successful implementation of DTs specifically, not previously considered by the theory. The study identifies promising avenues for future research. These findings provide invaluable guidance to researchers and practitioners seeking to embrace the potential of Digital Twin within the food industry. [ABSTRACT FROM AUTHOR]

Rapid advances in new-generation information technologies have been the main driving force for the transformation of manufacturing enterprises in Industry 4.0. Digital twin (DT), as a key technology to promote intelligent manufacturing, has shown great potential for manufacturing enterprises to create an industrial intelligence-driven production equipment through in-depth integration of cyber-physical systems. However, the lack of a systematic effective DT modelling method with a supporting evaluation metric is the most important factor restricting the application of DT in manufacturing enterprises. To bridge the gap, this paper proposes a novel multi-level modelling and fidelity evaluation (MLM&FE) method of DT for creating smart production equipment in manufacturing enterprises, which could help enterprises establish an industrial intelligence-driven production environment to quickly respond to changes in the customised global market, thus greatly improving competitiveness of the enterprises. Specifically, this paper firstly designs a reference framework for DT-enhanced smart production equipment, on which an MLM&FE architecture is proposed. Then, key implementation methodologies and tools for MLM&FE are introduced from the perspective of data space modelling, virtual space modelling, knowledge space modelling, model integration and evaluation. Finally, the developed smart production equipment prototype demonstrates the feasibility and effectiveness of DT MLM&FE. [ABSTRACT FROM AUTHOR]

The Industry 4.0 revolution is changing the manufacturing landscape. A broad set of new technologies emerged (including software and connected equipment) that digitise manufacturing systems. These technologies bring new vitality and opportunities to the manufacturing industry, but they also bring new challenges. This paper focuses on the impact of Industry 4.0 on production planning approaches and software. We first propose a digital twin framework that integrates production planning systems and frontier technologies. The frontier technologies that may impact production planning software are the internet of things, cloud manufacturing, blockchain, and big data analytics. Second, we provide a state-of-the-art on the application of each technology in the production planning, as well as a detailed analysis of the benefit and application status. Finally, this paper discusses the future research and application directions in the production planning. We conclude that Industry 4.0 will lead to the construction of data-driven models for production planning software. These tools will include models built accurately from data, account for uncertainty, and partially actuate the decision autonomously. [ABSTRACT FROM AUTHOR]

The concept of digital twins is one of the fundamental pillars of Industry 4.0. Digital twin allows the realization of a virtual model of a real system, enhancing the relevant performance (e.g., in terms of production rate, risk prevention, energy saving, and maintenance operation). Current literature presents many contributions pointing out the advantages that may be achieved by the definition of a digital twin of a water supply system. The Reference Architecture Model for Industry 4.0 introduces the concept of the Asset Administration Shell for the digital representation of components within the Industry 4.0 ecosystem. Several proposals are currently available in the literature considering the Asset Administration Shell for the realization of a digital twin of real systems. To the best of the authors' knowledge, at the moment, the adoption of Asset Administration Shell for the digital representation of a water supply system is not present in the current literature. For this reason, the aim of this paper is to present a methodological approach for developing a digital twin of a water supply system using the Asset Administration Shell metamodel. The paper will describe the approach proposed by the author and the relevant model based on Asset Administration Shell, pointing out that its implementation is freely available on the GitHub platform. [ABSTRACT FROM AUTHOR]

Feasibility checking is a step in manufacturing system engineering for verifying the normalization and effectiveness of a manufacturing system associated with a specific configuration of resources and processes. It enables factory operators to predict problems before operational time, thus preventing equipment and machinery accidents and reducing labor waste in physically organizing the shop floor. In Industry 4.0, feasibility checking becomes even more critical since emerging challenges, such as mass personalization, require reconfiguring work cells quickly and flexibly on demand. Regarding this need, digital twin technologies have emerged as an ideal candidate for practicing feasibility checking. Indeed, they are tools used to implement digital representations of manufacturing entities that can constitute a digital environment and context. Factory operators can test a manufacturing process within a digital environment in different contexts before the execution with physical resources. This approach currently receives significant attention from the manufacturing community; however, there is still a lack of sharing experiences to implement it. Thus, this paper contributes a methodology to engineer a digital environment and context for a manufacturing work cell using AAS digital twins and physics-based 3D digital twins technologies. Technically, this methodology is a specific case of N-DTs, a general methodology for engineering heterogeneous digital twins. The product assembly line case study, also presented in this paper, is a successful experiment applying the above contributions. The two methodologies and the case study can be helpful references for both public and private sectors to deploy their feasibility-checking frameworks and deal with heterogeneous digital twins in general. [ABSTRACT FROM AUTHOR]

Digital twin is the digital representation of an entity, and it drives Industry 4.0. This paper presents a bibliometric analysis of digital twin in the supply chain to help researchers, industry practitioners, and academics to understand the trend, development, and focus of the areas of digital twin in the supply chain. This paper found several key clusters of research, including the designing of a digital twin model, integration of a digital twin model, application of digital twin in quality control, and digital twin in digitalization. In the embryonic stage of research, digital twin was tested in the production line with limited optimization. In the development stage, the importance of digital twin in Industry 4.0 was observed, as big data, machine learning, Industrial Internet of Things, blockchain, edge computing, and cloud-based systems complemented digital twin models. Digital twin was applied to improve sustainability in manufacturing and production logistics. In the current prosperity stage with high annual publications, the recent trends of this topic focus on the integration of deep learning, data models, and artificial intelligence for digitalization. This bibliometric analysis also found that the COVID-19 pandemic drove the start of the prosperity stage of digital twin research in the supply chain. Researchers in this field are slowly moving towards applying digital twin for human-centric systems and mass personalization to prepare to transit to Industry 5.0. [ABSTRACT FROM AUTHOR]

Purpose: The primary objective of this paper is to examine the transformative effects of the Fourth Industrial Revolution (Industry 4.0) on marketing and management practices, using Nokia Poland as a case study. The research aims to understand how Industry 4.0 technologies can be leveraged to navigate challenges and capitalize on new opportunities within the telecommunications sector and beyond. Design/methodology/approach: The research adopts a qualitative case study approach, focusing on Nokia Poland to explore the application and impact of Industry 4.0 technologies in a real-world corporate setting. Data collection methods include semi-structured interviews with key Nokia Poland executives, analysis of company documents, and review of relevant literature on Industry 4.0. The study is framed within the broader theoretical context of digital transformation and innovation management. Findings: The results show that Nokia Poland has effectively incorporated Industry 4.0 technologies to boost effectiveness introduce product solutions and enhance customer interaction. Essential technologies such as the Internet of Things (IoT) artificial intelligence (AI), and digital twins have enabled faster. The study emphasizes the role of embracing a strategy for digital transformation that involves cultural shifts, skill enhancement, and collaboration, within the ecosystem. Research limitations/implications: The study scope is restricted to examining a telecommunications company potentially overlooking the experiences and approaches prevalent in other sectors. To gain an understanding of Industry 4.0 adoption future research should explore comparative analyses spanning various industries and regions. Additionally, the study highlights the importance of exploration in the changing landscape of technologies and their impact on businesses. Practical implications: The findings of this research provide advice for businesses starting their Industry 4.0 transformation. Suggestions involve creating a defined plan allocating resources to skills and technology and fostering collaborations in the innovation network. Additionally, the paper explores the business and marketing framework of Industry 4.0, highlighting opportunities for increased competitiveness and expansion in the market. Social implications: The research suggests that Industry 4.0 has significant implications for workforce development, requiring upskilling and reskilling initiatives to prepare employees for new digital roles. Additionally, the paper considers the potential of Industry 4.0 technologies to drive sustainable business practices and contribute to societal well-being through improved product quality and accessibility. Originality/value: This paper contributes to the existing literature by providing an in-depth case study of Industry 4.0 implementation in a leading multinational company. It offers practical insights and strategies for managing digital transformation, with a particular emphasis on the value of integrating advanced technologies in marketing and management. [ABSTRACT FROM AUTHOR]

The manufacturing industry faces a new revolution, grounded on the intense digitalization of assets and industrial processes and the increasing computation capabilities imposed by the new data-driven digital architectures. This reality has been promoting the Digital Twin concept and its importance in the industrial companies' business models. However, with these new opportunities, also new threads may rise, mainly related to industrial data protection and sovereignty. Therefore, this research paper will demonstrate the International Data Spaces reference model's application to overcome these limitations. Following a pilot study with a Portuguese machine producer/maintainer enterprise, this paper will demonstrate the development of a cutting and bending machine Digital Twin, leveraged on an International Data Spaces infrastructure for interoperability, for the plastic and metal industry and its importance to introduce this machine manufacturing company in a new business-to-business marketplace from the EU project Market 4.0. [ABSTRACT FROM AUTHOR]

Although standards and specifications for digital twins aim to create interoperability in Industry 4.0, each standard has its own goals, focuses and representations for digital twins. This paper examines an approach to increasing interoperability between established digital twin specifications by transformation. Accordingly, several specifications are presented and requirements for transformation are examined. Following the feasibility analysis, a mapping between the Digital Twin Definition Language (DTDL) and Asset Administration Shell (AAS) was created. To examine the feasibility of this approach, the transformation was implemented and tested for a physical asset. This paper demonstrates that a generic mapping between DTDL and AAS can be applied for transformation in use cases where DTDL models are provided while AAS is required. [ABSTRACT FROM AUTHOR]

The rapid development of digitalization, the Internet of Things (IoT), and Industry 4.0 has led to the emergence of the digital twin concept. IoT is an important pillar of the digital twin. The digital twin serves as a crucial link, merging the physical and digital territories of Industry 4.0. Digital twins are beneficial to numerous industries, providing the capability to perform advanced analytics, create detailed simulations, and facilitate informed decision-making that IoT supports. This paper presents a review of the literature on digital twins, discussing its concepts, definitions, frameworks, application methods, and challenges. The review spans various domains, including manufacturing, energy, agriculture, maintenance, construction, transportation, and smart cities in Industry 4.0. The present study suggests that the terminology "3 dimensional (3D) digital twin" is a more fitting descriptor for digital twin technology assisted by IoT. The aforementioned statement serves as the central argument of the study. This article advocates for a shift in terminology, replacing "digital twin" with "3D digital twin" to more accurately depict the technology's innate potential and capabilities in Industry 4.0. We aim to establish that "3D digital twin" offers a more precise and holistic representation of the technology. By doing so, we underline the digital twin's analytical ability and capacity to offer an intuitive understanding of systems, which can significantly streamline decision-making processes using the digital twin. [ABSTRACT FROM AUTHOR]

Research background: On the basis of an analysis of the current situation and expectations in the field of implementation of the elements of the Industry 4.0 concept, the purpose of this paper is to identify the effects on the labor market in large manufacturing enterprises in the Slovak Republic. Purpose of the article: The presented work has a theoretical-empirical nature and consists of a theoretical section and a practical section, which includes statistical indicator analysis and quantitative research. In the theoretical section, the paper discusses the issue of Industry 4.0 in general, with a focus on its impact on the labor market, thus laying the groundwork for future research on the subject. Methods: The output of this work is an analysis of selected indicators of the manufacturing industry sector in the Slovak Republic, based on the most recent employment data analysis in the first stage and quantitative research survey in the second stage, with the respondents being manufacturing industry companies operating in the Slovak Republic, and whose primary objective is to determine the current status of the implementation of the elements and technologies of Industry 4.0 in production companies in the Slovak Republic, as well as the factors influencing this situation, such as digital twin simulation modeling, artificial intelligence-based Internet of Manufacturing Things systems, and virtual machine and cognitive computing algorithms. Findings & value added: The research findings indicate that the degree of digitization adopted by businesses in the Slovak Republic is comparatively less robust and more sluggish to adapt. This is primarily attributable to the underdeveloped educational system, population reluctance, self-actualization, and inadequate state support. Recommendations for the Slovak market aim to increase the digital proficiency of businesses and of the general populace through various means, such as reforming legislation, enhancing state support for entrepreneurs, and modifying the education system, constituting the added value of the work. [ABSTRACT FROM AUTHOR]

The Fourth Industrial Revolution has had a huge impact on manufacturing processes and products. With rapidly growing technology, new solutions are being implemented in the field of digital representations of a physical product. This approach can provide benefits in terms of cost and testing time savings. In order to test and reflect the operation of an electric car, a digital twin model was designed. The paper collects all the information and standards necessary to transform the idea into a real and virtual model of an electric car. The significance and impact of the study on the improvement of the project are described. The research stand, correlations of components (DC and AC motors, shaft, and wheel of the electric car), and development prospects are presented in the paper. The communication method with the research stand is also presented. The digital twin should communicate in real time, which means obtaining the correct output when the input changes; the input is the AC motor current, and the output is the rotational speed of the DC motor. The relation between inputs and outputs are tested. The kinematics of the electric car are modelled in LabVIEW. The results obtained are compared with historic racing data. The track is also modeled based on satellite data, taking into account changes in terrain height, using the SG Telemetry Viewer application. The parameters of the electric car engine model are tuned based on actual data on the car's speed and current in the electric motor. The achieved results are presented and then discussed. [ABSTRACT FROM AUTHOR]

Digital twins (DTs) provide accurate, data-driven, real-time modeling to create a digital representation of the physical world. The integration of new technologies, such as virtual/mixed reality, artificial intelligence, and DTs, enables modeling and research into ways to achieve better sustainability, greater efficiency, and improved safety in Industry 4.0/5.0 technologies. This paper discusses concepts, limitations, future trends, and potential research directions to provide the infrastructure and underlying intelligence for large-scale semi-automated DT building environments. Grouping these technologies along these lines allows for a better consideration of their individual risk factors and use of available data, resulting in an approach to generate holistic virtual representations (DTs) to facilitate predictive analyses in industrial practices. Artificial intelligence-based DTs are becoming a new tool for monitoring, simulating, and optimizing systems, and the widespread implementation and mastery of this technology will lead to significant improvements in performance, reliability, and profitability. Despite advances, the aforementioned technology still requires research, improvement, and investment. This article's contribution is a concept that, if adopted instead of the traditional approach, can become standard practice rather than an advanced operation and can accelerate this development. [ABSTRACT FROM AUTHOR]

A digital twin (DT) is a virtual/digital model of any physical object (physical twin), interconnected through data exchange. In the context of Industry 4.0, DTs are integral to intelligent automation driving innovation at scale by providing significant improvements in precision, flexibility, and real-time responsiveness. A critical challenge in developing DTs is achieving a model that reflects real-time conditions with precision and flexibility. This paper focuses on evaluating latency and accuracy, key metrics for assessing the efficacy of a DT, which often hinder scalability and adaptability in robotic applications. This article presents a comprehensive framework for developing DTs using Unity and Robot Operating System (ROS) as the main layers of digitalization and communication. The MoveIt package was used for motion planning and execution for the robotic arm, showcasing the framework's versatility independent of proprietary constraints. Leveraging the versatility and open-source nature of these tools, the framework ensures interoperability, adaptability, and scalability, crucial for modern smart manufacturing applications. Our approach was validated by conducting extensive accuracy and latency tests. We measured latency by timestamping messages exchanged between the physical and digital twin, achieving a latency of 77.67 ms. Accuracy was assessed by comparing the joint positions of the DT and the physical robotic arm over multiple cycles, resulting in an accuracy rate of 99.99%. The results highlight the potential of DTs in enhancing operational efficiency and decision-making in manufacturing environments. [ABSTRACT FROM AUTHOR]

Implementing the IIoT concept in industry involves the development and implementation of online systems monitoring the technical state of electromechanical equipment. This is achieved through the use of digital twins and digital shadows (object state observers). The tasks of mastering new rolling profiles and optimizing plate mill rolling programs require improved methods for calculating equivalent motor currents and torques. Known methods are generally based on calculations using smoothed load diagrams, which are assumed to be identical for the upper and lower main drive (UMD and LMD) rolls. These methods do not consider the differences in actual loads (currents or torques) in steady rolling states. Experiments performed on the 5000 plate mill have shown that due to speed mismatches, the UMD and LMD torques differ three times or more. This causes overheating of the more heavily loaded motor, insulation life reduction, and premature failure. Therefore, the problem of developing and implementing techniques for monitoring the load and thermal regimes of motors using digital observers is relevant. The paper's contribution is the first justification of the concept of object-oriented digital shadows. They are developed for specific classes of industrial units using open-source software. This research justifies a methodology for assessing motor load and temperature by processing arrays of motor currents or torques generated during rolling. An equivalent load observer and a temperature observer were proposed and implemented using Matlab-Simulink resources. The algorithm was implemented on the mill 5000 and tuned using an earlier-developed virtual commissioning methodology with digital twins. Thermal regimes were studied, proving that torque alignment ensures equal motor temperatures. The proposed considerations contribute to the development of the theory and practice for creating digital systems to monitor the technical condition of electromechanical and mechatronic systems and implementing the Industry 4.0 concept at industrial enterprises. [ABSTRACT FROM AUTHOR]

Digital Twins (DT) play a key role in Industry 4.0 applications, and the technology is in the process of being mature. Since its conceptualisation, it has been heavily contextualised and often misinterpreted as being merely a virtual model. Thus, it is crucial to define it clearly and have a deeper understanding of its architecture, workflow, and implementation scales. This paper reviews the notion of a Digital Twin represented in the literature and analyses different kinds of descriptions, including several definitions and architectural models. A new fit-for-all definition is proposed which describes the underlying technology without being context-specific and also overcomes the pitfalls of the existing generalised definitions. In addition, the existing three-dimensional and five-dimensional models of the DT architecture and their characteristic features are analysed. A new simplified two-branched model of DT is introduced, which retains a clear separation between the real and virtual spaces and outlines the latter based on the two key modelling approaches. This model is then extended for condition monitoring of electronic components and systems, and a hybrid approach to Prognostics and Health Management (PHM) is further elaborated on. The proposed framework, enabled by the two-branched Digital Twin model, combines the physics-of-degradation and data-driven approaches and empowers the next generation of reliability assessment methods. Finally, the benefits, challenges, and outlook of the proposed approach are also discussed. [ABSTRACT FROM AUTHOR]

The use of open-source software is crucial for the digitalization of manufacturing, including the implementation of Digital Twins as envisioned in Industry 4.0. This research paper provides a comprehensive comparison of free and open-source implementations of the reactive Asset Administration Shell (AAS) for creating Digital Twins. A structured search on GitHub and Google Scholar was conducted, leading to the selection of four implementations for detailed analysis. Objective evaluation criteria were defined, and a testing framework was created to test support for the most common AAS model elements and API calls. The results show that all implementations support at least a minimal set of required features while none implement the specification in all details, which highlights the challenges of implementing the AAS specification and the incompatibility between different implementations. This paper is therefore the first attempt at a comprehensive comparison of AAS implementations and identifies potential areas for improvement in future implementations. It also provides valuable insights for software developers and researchers in the field of AAS-based Digital Twins. [ABSTRACT FROM AUTHOR]

In today's industry, the fourth industrial revolution is underway, characterized by the integration of advanced technologies such as artificial intelligence, the Internet of Things, and big data. One of the key pillars of this revolution is the technology of digital twin, which is rapidly gaining importance in various industries. However, the concept of digital twins is often misunderstood or misused as a buzzword, leading to confusion in its definition and applications. This observation inspired the authors of this paper to create their own demonstration applications that allow the control of both the real and virtual systems through automatic two-way communication and mutual influence in context of digital twins. The paper aims to demonstrate the use of digital twin technology aimed at discrete manufacturing events in two case studies. In order to create the digital twins for these case studies, the authors used technologies as Unity, Game4Automation, Siemens TIA portal, and Fishertechnik models. The first case study involves the creation of a digital twin for a production line model, while the second case study involves the virtual extension of a warehouse stacker using a digital twin. These case studies will form the basis for the creation of pilot courses for Industry 4.0 education and can be further modified for the development of Industry 4.0 educational materials and technical practice. In conclusion, selected technologies are affordable, which makes the presented methodologies and educational studies accessible to a wide range of researchers and solution developers tackling the issue of digital twins, with a focus on discrete manufacturing events. [ABSTRACT FROM AUTHOR]

Digital Twin (DT) aims to provide industrial companies with an interface to visualize, analyze, and simulate the production process, improving overall performance. This paper proposes to extend existing DT by adding a complementary methodology to make it suitable for process supervision. To implement our methodology, we introduce a novel framework that identifies, collects, and analyses data from the production system, enhancing DT functionalities. In our case study, we implemented Key Performance Indicators (KPIs) in the immersive environment to monitor physical processes through cyber representation. First, a review of the Digital Twin (DT) allows us to understand the status of the existing methodologies as well as the problem of data contextualization in recent years. Based on this review, performance data in Cyber–Physical Systems (CPS) are identified, localized, and processed to generate indicators for monitoring machine and production line performance through DT. Finally, a discussion reveals the difficulties of integration and the possibilities to respond to other major industrial challenges, like predictive maintenance. [ABSTRACT FROM AUTHOR]

The concept of Digital Twin is of fundamental importance to meet the main requirements of Industry 4.0. Among the standards currently available to realize Digital Twins there is the Digital Twins Definition Language. Digital Twin requires exchange of data with the real system it models and with other applications that use the digital replica of the system. In the context of Industry 4.0, a reference standard for an interoperable exchange of information between applications, is Open Platform Communications Unified Architecture. The authors believe that interoperability between Digital Twins and Open Platform Communications Unified Architectures communication standard should be enabled. For this reason, the main goal of this paper is to allow a Digital Twin based on the Digital Twins Definition Language to exchange data with any applications compliant to the Open Platform Communications Unified Architecture. A proposal about the mapping from Digital Twins Definition Language to the Open Platform Communications Unified Architecture will be presented. In order to verify the feasibility of the proposal, an implementation has been made by the authors, and its description will be introduced in the paper. Furthermore, the main results of the validation process accomplished on the basis of this implementation will be given. [ABSTRACT FROM AUTHOR]

This paper proposes a framework to automate the generation of traceable and protected documentation of complex assembly processes. The final assembly in aviation, automotive, and appliances industries is a rigorous process that has limited capabilities of full traceability associated with: (1) the parts installed, (2) their fabrication processes, and (3) the assembly work. This is also the case for each of its sub-assemblies. The thousands of parts forming a hierarchy of sub-assemblies that are dynamically accumulated to compose the final assembly make full traceability a challenging feat that is almost unsurmountable. Such full traceability along the entire supply chain requires considerable cost and effort since it must be based on documentation of most assembled parts, assembly tasks, and inspection tasks that compose the full assembled product. In addition, security measures are needed to prevent hostile hacking and unauthorized approach to the assembly documentation throughout the entire supply chain. The related documentation and repeated verifications require considerable effort and have many chances for human errors. So, automating these processes has great value. This article expounds a framework that harnesses blockchain and smart-contract technology to offer automated traceable and protected documentation of the assembly process. For this purpose, we expand the concept of a Bill-Of-Assembly (BOA) to incorporate data from the bill of materials (BOM), the associated assembly activities, the associated activities' specification parameters and materials, and the associated assembly resources (machines and/or operators). The paper defines the operation of the BOA with blockchain and smart-contract technology, for attaining full traceability, safety, and security, for the entire assembled product. Future research could extend the proposed approach to facilitate the usage of the BOA data structure in constructing a digital twin of the entire simulated system. [ABSTRACT FROM AUTHOR]

Sawmills are key elements of the forest product industry supply chain, and they play important economic, social, and environmental roles. Sawmill production planning and control are, however, challenging owing to several factors, including, but not limited to, the heterogeneity of the raw material. The emerging concept of digital twins introduced in the context of Industry 4.0 has generated high interest and has been studied in a variety of domains, including production planning and control. In this paper, we investigate the benefits digital twins would bring to the sawmill industry via a literature review on the wider subject of sawmill production planning and control. Opportunities facilitating their implementation, as well as ongoing challenges from both academic and industrial perspectives, are also studied. [ABSTRACT FROM AUTHOR]

Purpose: The digital warehouse management system is an emergence that forms a critical part of the transformation of economic structure in Industry 4.0. In the present business scenario, the warehouse management system encounters a messy layout, poor damage control, unsatisfactory order management, lack of visibility and lack of technological interventions. Digital twin (DT) based warehouse system shows the ontology and knowledge graphs for competitive advantage by consolidating and transferring goods directly from an inbound supplier to an outbound customer on short notice and with no or limited storage. There remains a lack of clarity on how the DT can be implemented successfully in warehouse management. Design/methodology/approach: The current literature remains largely unstructured and scattered due to a lack of a systematic approach to integrating the research implications and analysis. This paper probes the conceptualization of the DT with the help of theoretical analysis using the systematic literature analysis method. Findings: The study explores essential concepts such as interoperability and integrability in implementing DT. Further, it analyzes the role of a supply chain control tower (SCCT) in modern supply chain management. A research framework is proposed for practitioners and academicians by incorporating the opportunities and challenges associated with DT implementation. The research findings are mainly threefold: Conceptualization of DT, Featuring SCCT and Exploration of cross-computer platform interfaces, scalability and maintenance strategies. Originality/value: This study is among the first to analyze and review DT applications in warehouse management. Moreover, the study proposes a theoretical toolbox for the practitioners to successfully implement the DT in warehouse DT-based warehouse management system: A theoretical toolbox for future research and applications. [ABSTRACT FROM AUTHOR]

One of the goals of developing and implementing Industry 4.0 solutions is to significantly increase the level of flexibility and autonomy of production systems. It is intended to provide the possibility of self-reconfiguration of systems to create more efficient and adaptive manufacturing processes. Achieving such goals requires the comprehensive integration of digital technologies with real production processes towards the creation of the so-called Cyber–Physical Production Systems (CPPSs). Their architecture is based on physical and cybernetic elements, with a digital twin as the central element of the "cyber" layer. However, for the responses obtained from the cyber layer, to allow for a quick response to changes in the environment of the production system, its virtual counterpart must be supplemented with advanced analytical modules. This paper proposes the method of creating a digital twin production system based on discrete simulation models integrated with deep reinforcement learning (DRL) techniques for CPPSs. Here, the digital twin is the environment with which the reinforcement learning agent communicates to find a strategy for allocating processes to production resources. Asynchronous Advantage Actor–Critic and Proximal Policy Optimization algorithms were selected for this research. [ABSTRACT FROM AUTHOR]

In traditional injection molding, each level of the process has its own monitoring and improvement initiatives. But in the upcoming industrial revolution, it is important to establish connections and communication among all stages, as changes in one stage might have an impact on others. To address this issue, digital twins (DTs) are introduced as virtual models that replicate the entire injection molding process. This paper focuses on the data and technology needed to build a DT model for injection molding. Each stage can have its own DT, which are integrated into a comprehensive model of the process. DTs enable the smart automation of production processes and data collection, reducing manual efforts in supervising and controlling production systems. However, implementing DTs is challenging and requires effort for conception and integration with the represented systems. To mitigate this, the current work presents a model for systematic knowledge-based engineering for the DTs of injection molding. This model includes fault detection systems, 3D printing, and system integration to automate development activities. Based on knowledge engineering, data analysis, and data mapping, the proposed DT model allows fault detection, prognostic maintenance, and predictive manufacturing. [ABSTRACT FROM AUTHOR]

The additive manufacturing (AM) field is rapidly expanding, attracting significant scientific attention. This family of processes will be widely used in the evolution of Industry 4.0, particularly in the production of customized components. However, as the complexity and variability of additive manufacturing processes increase, there is an increasing need for advanced techniques to ensure quality control, optimize performance, and reduce production costs. Multiple tests are required to optimize processing variables for specific equipment and processes, to achieve optimum processing conditions. The application of digital twins (DTs) has significantly enhanced the field of additive manufacturing. A digital twin, abbreviated as DT, refers to a computer-generated model that accurately depicts a real-world object, system, or process. A DT comprises the complete additive manufacturing process, from the initial conception phase to the final manufacturing phase. It enables the manufacturing process to be continuously monitored, studied, and optimized in real time. DT has emerged as an important tool in the additive manufacturing industry. They allow manufacturers to enhance the process, improve product quality, decrease costs, and accelerate innovation. However, the development of DT in AM is an iterative and continuous process. It requires collaboration between domain experts, data scientists, engineers, and manufacturing teams to guarantee an accurate representation of the process by the digital twin. This paper aims to provide a comprehensive analysis of the current state of DT for additive manufacturing, examining their applications, benefits, challenges, and future directions. [ABSTRACT FROM AUTHOR]

Purpose: The digital twin (DT) has become a heated topic among supply chain and information technology practitioners. While many papers in this area focus on technical tactics and learnings, this research paper aims to evaluate its business implications. According to literature, it has also been a weakly covered topic. Design/methodology/approach: The research was conducted as a single case study, in which the impact of radio-frequency identification–enabled DT was quantified from the business benefits perspective. The evaluation was carried out using a framework model developed for the assessment identifying key contribution areas and the dynamics explaining how the benefits are expected to land on a business level. Findings: Implementation of the DT was calculated to provide a significant supply chain performance improvement. The main contributor in the immediate benefits was the reduction in supply chain costs, in person-hours. However, the product availability improvement was conservatively considered in the evaluation, and thus, this paper estimates that it, together with higher cognition tools, constitutes the main financial return in the long run showing in the topline improvement. This paper suggests that the shift to DT can be generally limited by the cost savings perspective. Originality/value: To the best of the authors' knowledge, this is one of the first studies released on the business impact of the cutting-edge technical solution area of the DT in supply chain management. In practice, businesses require an understanding of the business implications to decide on the investments in this area; thus, it is a critical part of the discussion. [ABSTRACT FROM AUTHOR]

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision- and policy-making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This second paper presents a literature review of key enabling technologies of digital twins, with an emphasis on uncertainty quantification, optimization methods, open-source datasets and tools, major findings, challenges, and future directions. Discussions focus on current methods of uncertainty quantification and optimization and how they are applied in different dimensions of a digital twin. Additionally, this paper presents a case study where a battery digital twin is constructed and tested to illustrate some of the modeling and twinning methods reviewed in this two-part review. Code and preprocessed data for generating all the results and figures presented in the case study are available on Github. [ABSTRACT FROM AUTHOR]

As a key enabling technology of Industry 4.0, Digital Twin (DT) has been widely applied to various industrial domains covering different lifecycle phases of products and systems. To fully realize the Industry 4.0 vision, it is necessary to integrate multiple relevant DTs of a system according to a specific mission. This requires integrating all available data, information and knowledge related to the system across its entire lifecycle. It is a challenging task due to the high complexity of modern industrial systems. Semantic technologies such as ontology and knowledge graphs provide potential solutions by empowering DTs with augmented cognitive capabilities. The Cognitive Digital Twin (CDT) concept has been recently proposed which reveals a promising evolution of the current DT concept towards a more intelligent, comprehensive, and full lifecycle representation of complex systems. This paper reviews existing studies relevant to the CDT concept, and further explores its definitions and key features. To facilitate CDT development, a reference architecture is proposed based on the RAMI4.0 and some other existing architectures. Moreover, some key enabling technologies and several application scenarios of CDT are introduced. The challenges and opportunities are discussed in the end to boost future studies. [ABSTRACT FROM AUTHOR]

This paper reviews the applications of Internet of Things (IoT) and digital twin technology in electrical power systems. It begins by discussing the generalized IoT value chain, followed by the terminology of smart grid, with clarifying the role of IoT‐systems and the digital twin structure within the Smart Grid. A comparison between different short‐range and long‐range transports is presented. The paper then discusses the use of IoT and digital twin technology for effective energy management with applications in smart homes, smart buildings, smart grids, smart industries (Industry 4.0), smart transportation and smart cities. Additionally, the paper explores the use of IoT and digital twin technology for condition monitoring and diagnosis (CMD) in electrical power systems. Three different cases are presented for CMD, that is, CMD of power transformers, CMD of electrical grids and CMD of substations. IoT and digital twin applications are also highlighted in power electronic systems. Finally, the paper discusses the challenges and opportunities of applying IoT and digital twin technology to electrical power systems and provides recommendations for future research. [ABSTRACT FROM AUTHOR]

Industry 4.0 supports the vision of networked machines in decentralized production plants across the value chain. Hence, it requires highly connected partners exchanging relevant data about products, processes, and production resources. This paper proposes the usage of data spaces and digital twins to enable this Industry 4.0 vision and investigates the building blocks to realize a data space for Industry 4.0, e.g., the integration of digital twins inside the data space based upon the latest specification of the Industry 4.0 Asset Administration Shell. A prototypical implementation shows the feasibility of storing product carbon footprints inside a digital twin and sharing it over a data space with other partners. [ABSTRACT FROM AUTHOR]

With the development of new technologies such as artificial intelligence and big data, Industry 4.0 in manufacturing has been launched. As the core pillar of industrial manufacturing, computer numerical control (CNC) machine tools face significant challenges in data acquisition transmission and storage due to their complex structure, high volume of data points, strong time-series characteristics, and large amounts of data. To address the shortcomings of existing compression algorithms in quantization methods for large amounts of data in the instruction-domain, this paper proposes a quantization method based on distortion rate evaluation and linear fitting entropy reduction transformation, which aims to compress state signals such as the load power and load current while ensuring the availability of the data. This approach provides technical support for the transmission of high-frequency big data and meets the lightweight data acquisition requirements of digital twins for CNC machine tools. Compared to the empirical approach, this approach was more accurate and more computationally efficient. [ABSTRACT FROM AUTHOR]

This paper explores the potential and limitations of machine learning for sensor signal identification in complex industrial systems. The objective is a tool to assist engineers in finding the correct inputs to digital twins and simulations from a set of unlabeled sensor signals. A naive end-to-end machine learning approach is usually not applicable to this task, as it would require many comparable industrial systems to learn from. We present a semi-structured approach that uses observations from the manual classification of time series and combines different algorithms to partition the set of signals into smaller groups of signals that share common characteristics. Using a real-world dataset from several power plants, we evaluate our solution for scaling-invariant measurement identification and functional relationship inference using change-point correlations. [ABSTRACT FROM AUTHOR]

Industry 4.0 has been provided for the last 10 years to benefit the industry and the shortcomings; finally, the time for industry 5.0 has arrived. Smart factories are increasing the business productivity; therefore, industry 4.0 has limitations. In this paper, there is a discussion of the industry 5.0 opportunities as well as limitations and the future research prospects. Industry 5.0 is changing paradigm and brings the resolution since it will decrease emphasis on the technology and assume that the potential for progress is based on collaboration among the humans and machines. The industrial revolution is improving customer satisfaction by utilizing personalized products. In modern business with the paid technological developments, industry 5.0 is required for gaining competitive advantages as well as economic growth for the factory. The paper is aimed to analyze the potential applications of industry 5.0. At first, there is a discussion of the definitions of industry 5.0 and advanced technologies required in this industry revolution. There is also discussion of the applications enabled in industry 5.0 like healthcare, supply chain, production in manufacturing, cloud manufacturing, etc. The technologies discussed in this paper are big data analytics, Internet of Things, collaborative robots, Blockchain, digital twins and future 6G systems. The study also included difficulties and issues examined in this paper head to comprehend the issues caused by organizations among the robots and people in the assembly line. [ABSTRACT FROM AUTHOR]

This paper proposes to combine the concept of digital twins with the concept of dataspaces to fulfill the original expectation that a digital twin is a comprehensive virtual representation of physical assets. Based upon a terminological and conceptual discussion of digital twins and dataspaces, this paper claims that a systemic approach towards digital twin Systems is required. The key conceptual approach consists of a Reference Model for Digital Twin Systems (DTS-RM) and a hypothesis regarding a symbiotic evolution. The DTS-RM distinguishes between a digital twin back-end platform comprising the access and management of comprehensive digital twin instances and digital twin-related services, and digital twin front-end services that are tailored to the demands of applications and users. The main purpose of the back-end platform is to decouple the digital twin's generation and management from the usage of the digital twin for applications. [ABSTRACT FROM AUTHOR]

Blockchain technology (BCT) can enable distributed collaboration, enhance data sharing, and automate back-end processes for digital twin (DT) decentralized applications (dApps) in the construction industry (CI) 4.0. The aim of this paper was to propose a software architecture and to develop a framework of smart contracts for blockchain-based digital twin (BCDT) dApps throughout the lifecycle of projects in CI 4.0. This paper leveraged the existing literature and action research interviews to identify and validate the critical industry problems, functional requirements (FRs), and non-functional requirements (NFRs) to be addressed by BCDT dApps in CI 4.0. Basic use cases were developed to design a framework of smart contracts for BCDT dApps throughout the lifecycle of projects. The analysis of an online survey was used to identify the key requirements and enablers to propose a software architecture for BCDT applications and to validate the requirements for developing the framework of a smart contract for BCDTs. The findings were: (1) The identification of key problems in CI 4.0 for each BIM/BCDT dimension (3D, 4D, 5D, 6D, 7D, 8D, and contractual (cD)) and the related FRs and NFRs for BCDT applications. Additionally, key use cases were designed to address the problems identified. (2) The proposed BCDT architecture permitted us to narrow gaps in the literature on blockchain-based decentralized digital twins. Moreover, the proposed BCDT architecture and smart-contract framework addressed the main requirements in the literature on BCDTs. (3) The study leveraged the non-fungible token (NFT) standard to develop a framework for smart contracts that addressed the key use cases and the related industry problems and functional requirements that were identified. The study also considered the contractual dimension (cD) as an overarching dimension in relation to the other BCDT dimensions. (4) We also compared the costs of several public blockchains for executing the proposed smart-contract framework throughout the lifecycle of a medium-sized building project. The cost analysis permitted the development of criteria to evaluate the suitability of blockchain networks for BCDT applications in CI 4.0 depending on the principal blockchain networks' properties (security, decentralization, scalability, and interoperability). Finally, this study resulted in a novel framework that included software architecture, smart-contract use cases, and selection criteria among blockchain networks for BCDT dApps in CI 4.0. [ABSTRACT FROM AUTHOR]

Automated disassembly is increasingly in focus for Recycling, Re-use, and Remanufacturing (Re-X) activities. Trends in digitalization, in particular digital twin (DT) technologies and the digital product passport, as well as recently proposed European legislation such as the Net Zero and the Critical materials Acts will accelerate digitalization of product documentation and factory processes. In this contribution we look beyond these activities by discussing digital information for stakeholders at the Re-X segment of the value-chain. Furthermore, we present an approach to automated product disassembly based on different levels of available product information. The challenges for automated disassembly and the subsequent requirements on modeling of disassembly processes and product states for electronic waste are examined. The authors use a top-down (e.g., review of existing standards and process definitions) methodology to define an initial data model for disassembly processes. An additional bottom-up approach, whereby 5 exemplary electronics products were manually disassembled, was employed to analyze the efficacy of the initial data model and to offer improvements. This paper reports on our suggested informal data models for automatic electronics disassembly and the associated robotic skills. [ABSTRACT FROM AUTHOR]

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This paper delves into the application of digital twin monitoring techniques for enhancing offshore floating wind turbine performance, with a detailed case study that uses open-source digital twin software. We explore the practical implementation of digital twins and their efficacy in optimizing operations and predictive maintenance, focusing on controlling the real-time structural state of composite wind turbine structures and forecasting the remaining useful life by tracking the fatigue state in the structure. Our findings emphasize digital twins' potential as a valuable tool for renewable energy, driving efficiency and sustainability in offshore floating wind installations. These aspects, along with the aforementioned simulations, whether in real-time or forecasted, reduce costly and unnecessary inspections and scheduled maintenance. [ABSTRACT FROM AUTHOR]