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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]

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]

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]

Standing on the brink of the fourth industrial revolution, Cyber Physical Systems (CPS) are considered the basic components of the Smart Factory. One important challenge in cyber physical production systems is dynamic scheduling that can handle random disruptions such as failures, raw material shortages and quality defects. To achieve dynamic scheduling, we have proposed a Supervised and Distributed Holonic architecture we called SUDIHA. This architecture incorporates three Holons: Product Holon, Resource Holon and Order Holon and combines global supervision, achieved by Product Holon, with dynamic local control, achieved by Resource Holon. The Digital Twin (DT) concept is generally used to design CPS; it is virtual copies of the system that can interact with the physical counterparts in a bi-directional way. It seems to be promising to tackle the complexity and increase manufacturing system flexibility. In this paper, we use a DT Model to improve the SUDIHA architecture. We propose a Digital Twin based SUDIHA architecture (DT-SUDIHA). The paper will describe Digital Twins' configuration of each Holon of the SUDIHA Architecture, and the intelligent and real time data driven operation control of this architecture. A case study is carried out at the ENSAM-Meknes flexible workshop to prove the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The concept of digital twinning is essential for smart manufacturing and cyber-physical systems to be connected to the Metaverse. These digital representations of physical objects can be used for real-time analysis, simulations, and predictive maintenance. A combination of smart manufacturing, Industry 4.0, and the Metaverse can lead to sustainable productivity in industries. This paper presents a practical approach to implementing digital twins of a magnetic forging holder that was designed and manufactured in this project. Thus, this paper makes two important contributions: the first contribution is the manufacturing of the holder, and the second significant contribution is the creation of its digital twin. The holder benefits from a special design and implementation, making it a user-friendly and powerful tool in materials research. More specifically, it can be employed for the thermomechanical influencing of the structure and, hence, the final properties of the materials under development. In addition, this mechanism allows us to produce a new type of creep-resistant composite material based on Fe, Al, and Y. The magnetic forging holder consolidates the powder material to form a solid state after mechanical alloying. We produce bars from the powder components using a suitable forging process in which extreme grain coarsening occurs after the final heat treatment. This is one of the conditions for achieving very high resistance to creep at high temperatures. [ABSTRACT FROM AUTHOR]

With the rapid advent of new information technologies (Big Data analytics, cyber-physical systems, such as IoT, cloud computing and artificial intelligence), digital twins are being used more and more in smart manufacturing. Despite the fact that their use in industry has attracted the attention of many practitioners and researchers, there is still a need for an integrated and comprehensive digital twin framework for reconfigurable manufacturing systems. To close this research gap, we present evidence from a systematic literature review, including 76 papers from high-quality journals. This paper presents the current research trends on evaluation and the digital twin in reconfigurable manufacturing systems, highlighting application areas and key methodologies and tools. The originality of this paper lies in its proposal of interesting avenues for future research on the integration of the digital twin in the evaluation of RMS. The benefits of digital twins are multiple such as evaluation of current and future capabilities of an RMS during its life cycle, early discovery of system performance deficiencies and production optimization. The idea is to implement a digital twin that links the virtual and physical environments. Finally, important issues and emerging trends in the literature are highlighted to encourage researchers and practitioners to develop studies in this area that are strongly related to the Industry 4.0 environment. [ABSTRACT FROM AUTHOR]

Gear transmissions are relatively simple mechanisms whose quality, price, delivery time and design appearance are almost equal. This is the common reason why it is very difficult for all gear manufacturers to ensure the successful placement of their products on the market. However, regardless of their simplicity, gear transmissions may have different design and technical solutions, and due to different limitations of individual components, the calculation of load capacity and other technical characteristics can be quite complicated. In accordance with the basic settings of Industry 4.0, smart transmissions are being developed that will be able to provide a certain advantage over the competition and thus, for sure, ensure the successful business of these innovative companies. This paper describes the basic principles of construction using new technologies, primarily using the Internet of Things and digital twins. The paper includes an analysis of approaches to the creation of Digital Twins. Describe why Industry 4.0 is so different and specific, define what is Cyber-physical systems and the Internet of Things, and what is predictive monitoring with using different sensors. [ABSTRACT FROM AUTHOR]

Cyber physical systems, as a backbone of the Fourth Industrial Revolution (IR 4.0), and all their enabling technologies introduced a relatively newer concept named the digital twin. The interest in the digital twin technology has been growing in academia and industry. This is evident in the increasing number of published research and patents concerning digital twin development along with their various industrial applications. However, there is no framework that could be used to evaluate current digital twin development techniques available in the literature and, Using the proposed framework, identify corresponding strengths and shortfalls. In this paper, prominent approaches related to the development of digital twin were analyzed. Accordingly, a framework was built to compare between the digital twin approaches in terms of research domains, technologies, and models employed in the digital twin, and validation methods used. Using the proposed framework, gaps and future directions for digital twin research are identified from five aspects: digital twin definitions, applications, integration, modeling, and data. [ABSTRACT FROM AUTHOR]

The digital twin has recently become a popular topic in research related to manufacturing, such as Industry 4.0, the industrial internet of things, and cyber-physical systems. In addition, digital twins are the focus of several research areas: construction, urban management, digital transformation of the economy, medicine, virtual reality, software testing, and others. The concept is not yet fully defined, its scope seems unlimited, and the topic is relatively new; all this can present a barrier to research. The main goal of this paper is to develop a proper methodology for visualizing the digital-twin science landscape using modern bibliometric tools, text-mining and topic-modeling, based on machine learning models—Latent Dirichlet Allocation (LDA) and BERTopic (Bidirectional Encoder Representations from Transformers). The scope of the study includes 8693 publications on the topic selected from the Scopus database, published between January 1993 and September 2022. Keyword co-occurrence analysis and topic-modeling indicate that studies on digital twins are still in the early stage of development. At the same time, the core of the topic is growing, and some topic clusters are emerging. More than 100 topics can be identified; the most popular and fastest-growing topic is 'digital twins of industrial robots, production lines and objects.' Further efforts are needed to verify the proposed methodology, which can be achieved by analyzing other research fields. [ABSTRACT FROM AUTHOR]

The potential benefits of Industry 4.0 have led to an increased interest in smart manufacturing. To facilitate the self-diagnosis and adaptive ability in smart milling system, a digital twin–driven intelligent algorithm for monitoring in-process milling parameters is proposed here. The algorithm can extract the radial width of cut, axial depth of cut, cutter runout parameters, and cutting constants in the end milling process at the same time only by using force sensor. It is an important breakthrough in this paper to converge two different force models to realize cyber-physical fusion for identifying milling parameters in the milling process. By using the convolution force model, digital twin technology can extract the approximate solution of milling parameters in the machining process in advance, so as to narrow the range of solution. Furthermore, the subsequent artificial intelligence algorithm can find the accurate solution of the current milling parameters in a short calculation time by cyber-physical fusion with the numerical force model considering cutter runout effect. Milling experiments are carried out to validate the proposed algorithm. It is shown that due to the complementary advantages of the convolution force model and numerical force model, the algorithm proposed in this paper can give consider to the identification accuracy and calculation efficiency. [ABSTRACT FROM AUTHOR]

Real-time simulation and digital twin (DT) as a part of Industry 4.0 are becoming increasingly relevant, especially when considering production cycles. Most issues with production cycles arise from having a demand for customized production orders, while having nonmodular production lines with a medium-to-high complexity in the decision-making process. All these conditions lead to a possibility of unpredictable consequences. Being able to predict behavior and possible failure scenarios before the production starts has proven to save both costs and time. With an introduction of a new ISO standard which is solely focused on DT creation and sets a starting point for future research, researchers are finally able to focus on creating DT prototypes built for specific scenarios while maintaining the core concepts. This paper focuses on proposing strategies for DT and real-time simulation integration into production cycles, based on the new standards, which can be generalized and applied on a multitude of different systems with minimal changes. The proposed solutions offer different levels of human interaction with the Human–Machine Interfaces used in Cyber–Physical Systems created as a part of DT. Applicability of the solution has been verified based on the results of experiments carried out on the WITNESS Horizon simulation platform with utilization of the custom Order Manipulation Interface (OMI) application. [ABSTRACT FROM AUTHOR]

This paper aims to promote a quantum framework that analyzes Industry 4.0 cyber-physical systems more efficiently than traditional simulations used to represent integrated systems. The paper proposes a novel configuration of distributed quantum circuits in multilayered complex networks that enable the evaluation of industrial value creation chains. In particular, two different mechanisms for the integration of information between circuits operating at different layers are proposed, where their behavior is analyzed and compared with the classical conditional probability tables linked to the Bayesian networks. With the proposed method, both linear and nonlinear behaviors become possible while the complexity remains bounded. Applications in the case of Industry 4.0 are discussed when a component's health is under consideration, where the effect of integration between different quantum cyber-physical digital twin models appears as a relevant implication. [ABSTRACT FROM AUTHOR]

Industry 4.0, as an enabler of smart factories, focuses on flexible automation and customization of products by utilizing technologies such as the Internet of Things and cyber–physical systems. These technologies can also support the creation of virtual replicas which exhibit real-time characteristics of a physical system. These virtual replicas are commonly referred to as digital twins. With the increased adoption of digitized products, processes and services across manufacturing sectors, digital twins will play an important role throughout the entire product lifecycle. At the same time, collaborative robots have begun to make their way onto the shop floor to aid operators in completing tasks through human–robot collaboration. Therefore, the focus of this paper is to provide insights into approaches used to create digital twins of human–robot collaboration and the challenges in developing these digital twins. A review of different approaches for the creation of digital twins is presented, and the function and importance of digital twins in human–robot collaboration scenarios are described. Finally, the paper discusses the challenges of creating a digital twin, in particular the complexities of modelling the digital twin of human–robot collaboration and the exactness of the digital twin with respect to the physical system. [ABSTRACT FROM AUTHOR]

The evolution of computing science and simulation tools led to the usage of virtualization in the industrial environment. Software models became relevant information providers for the evaluation of automation processes. Digital twins (DT) are an approach for intercommunicating physical and virtual systems. DTs are intended to improve the performance of real systems by using information generated on a virtual system ("twin") that mirrors the behavior of physical parts. This work presents a distributed framework for DT implementation, which is composed of five integrated modules. The modules' functions are process control , logical modeling, 3D visualization and data analysis. This paper presents the implementation and performance analysis of the DT framework. A discussion about its potentials and limitations is presented as well. The analysis focuses on timing and communication latency among the DT modules. A case study implementation, composed of the DTs of two manufacturing processes, is presented to validate the operation and to provide data for performance analysis of the DT framework. [ABSTRACT FROM AUTHOR]

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In recent years, Industry 4.0 has provided many tools to replicate, monitor, and control physical systems. The purpose is to connect production assets to build cyber-physical systems that ensure the safety, quality, and efficiency of production processes. Particularly, the concept of digital twins has been introduced to create the virtual representation of physical systems where both elements are connected to exchange information. This general definition encompasses a series of major challenges for the developers of those functionalities. Among them is how to introduce the human perspective into the virtual replica. Therefore, this paper presents an approach for incorporating human factors in digital twins. This approach introduces a methodology to offer suggestions about employee rotations based on their previous performance during a shift. Afterward, this method is integrated into a digital twin to perform human performance assessments to manage workers' jobs. Furthermore, the presented approach is mainly comprised of a human skills modelling engine and a human scheduling engine. Finally, for demonstrating the approach, a simulated serial single-product manufacturing assembly line has been introduced. [ABSTRACT FROM AUTHOR]

With the recent remarkable development of information and communication technology, the “Fourth Industrial Revolution” is just about to take place. The fourth industrial revolution brings about a drastic change in manufacturing through cyber-physical systems. The cyber-physical system is referred to as the digital twin. The use of the digital twin will be indispensable in the trend toward high-mix low-volume production and mass customization. Undoubtedly, computer simulation will play an important role in the construction of the digital twin. The digital twin-based manufacturing will be used in powder products exactly. To construct the digital twin of the powder process, the Discrete Element Method will be employed, herein a highly efficient computational model, a computational model with high reproducibility of realistic phenomena, and reflection from the real world are required. In addition to developing computational models, analysis of big data in cyberspace and development of surrogate models are necessary as well. From the background, this paper aims to show my latest studies regarding the core technologies for the digital twin. [ABSTRACT FROM AUTHOR]

One of the keys to the success of the fourth industrial revolution (Industry 4.0) is to empower machinery with cyber–physical systems connectivity. The digital twin (DT) offers a promising solution to tackle the challenges for realizing digital and smart manufacturing which has been successfully projected in many scenes. Electrical machines and drive systems, as the core power providers in many appliances and industrial equipment, are supposed to be reinforced on the verge of Industry 4.0 in the fields of design optimization, fault prognostic and coordinated control. Therefore, this paper aims to investigate the DT modelling method and the applications in electrical drive systems. Firstly, taking the high-speed permanent-magnet machine drive system as an example, multi-disciplinary design fundamentals and technologies, aiming at building initial mechanism and simulation models, are reviewed. The state-of-the-art of DT technologies is figured out to serve for high-precision and multi-scale dynamic modelling, by which a framework for DT models of electrical drive systems is presented. More importantly, fault diagnosis and optimization strategies of electrical drive systems in the decision and application layer are also discussed for the DT models, followed by the conclusions presenting open questions and possible directions. [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]

A Digital Twin (DT) is a set of computer-generated models that map a physical object into a virtual space. Both physical and virtual elements exchange information to monitor, simulate, predict, diagnose and control the state and behavior of the physical object within the virtual space. DTs supply a system with information and operating status, providing capabilities to create new business models. In this paper, we focus on the construction of DTs. More specifically, we focus on determining (methodologically) how to design, create and connect physical objects with their virtual counterpart. We explore the problem into several phases: from functional requirement selection and architecture planning to integration and verification of the final (digital) models. We address as well how physical components exchange real-time information with DTs, as well as experimental platforms to build DTs (including protocols and standards). We conclude with a discussion and open challenges. [ABSTRACT FROM AUTHOR]

With the ongoing Industry 4.0 (I4.0) revolution, plant management and supervision play a key role in the development and (re-)design of industrial plants. In the arising scenarios, the need to coordinate human workers and autonomous systems, sharing the same environment, teaming together, becomes a fundamental requirement. Indeed, even though automation in standard assembly lines has reached high efficiency and reliability, for complex and new applications, a certain amount of failure must be considered for future addressing. This paper presents a framework for the flexible coordination of such a complex and heterogeneous cyber-physical system. A digital Ttwin mirrors in real-time the plant system, while a dashboard displays plant status, providing the human operators with fundamental tools for supervision and prompt intervention in case of failure. The framework was developed and tested in an industrially relevant environment, specifically for the assembly of the interior of an aircraft fuselage. [ABSTRACT FROM AUTHOR]

The construction industry continues to seek innovative ways to safely, timely and cost-effectively deliver construction projects. Several efforts have been made to automate construction processes but marginial success has been achieved in effectively reducing the long standing risks suffered by the industry. While industry 4.0 promises to improve project efficiency, reduce waste and improve productivity, the transition to this will depend on the successful adoption of many emerging technologies such as virtual design modeling technologies, sensing technologies, data analysis, storage and communication technologies, human-computer interaction technologies, and robotics. To accelerate innovation, digital twins and cyber-physical systems will be a necessity to advance automation and real-time control with these technologies. While digital twin represents a digital replica of the asplanned and as-built facility, cyber physical systems involve integration of physical systems with their digital replica through sensors and actuators. Despite evidence of the efficacy of cyber-physical systems and digital twins for reducing non-fatal injuries, enhancing safety management, improving progress monitoring and enhancing performance monitoring and control of facilities, their adoption in the construction industry is still in its infancy. This paper sheds light on the opportunities offered by cyber-physical systems and digital twins in other industry sectors and advocates for their increased deployment in the construction industry. This paper describes cyber-physical integration of emerging technologies with the physical construction or constructed facility as the next generation digital twin and cyber-physical systems. Potential scenarios of next generation cyber physical system and digital twin for improving workforce productivity, health, and safety, lifecycle management of building systems, and workforce competency are presented. [ABSTRACT FROM AUTHOR]

Industry 4.0, cyber-physical production systems (CPPS) and the Internet of Things (IoT) are current focusses in automation and data exchange in manufacturing, arising from the rapid increase in capabilities in information and communication technologies and the ubiquitous internet. A key enabler for the advances promised by CPPSs is the concept of a digital twin, which is the virtual representation of a real-world entity, or the physical twin. An important step towards the success of Industry 4.0 is the establishment of practical reference architectures. This paper presents an architecture for such a digital twin, which enables the exchange of data and information between a remote emulation or simulation and the physical twin. The architecture comprises different layers, including a local data layer, an IoT Gateway layer, cloud-based databases and a layer containing emulations and simulations. The architecture can be implemented in new and legacy production facilities, with a minimal disruption of current installations. This architecture provides a service-based and real-time enabled infrastructure for vertical and horizontal integration. To evaluate the architecture, it was implemented for a small, but typical, physical manufacturing system component. [ABSTRACT FROM AUTHOR]

Digital twin (DT) technology provides a path for implementing cyber–physical systems (CPS) and developing smart manufacturing because they are essential tools for monitoring and controlling manufacturing processes. It is considered a vital technology in smart manufacturing and is being widely researched in academia and industry. Furthermore, the combination of DTs and immersive environments has shown great potential for integrating novel capabilities into the new generation of CPS. This research presents an architecture for implementing immersive digital twins under ISO 23247 in flexible manufacturing processes. The proposed system is based on the integration of DT technologies in conjunction with augmented reality (AR) and gesture tracking, and validation was performed in the sorting station of the MPS 500 to increase the interaction and flexibility between physical and virtual environments in real time, thus enhancing the capabilities of the DT. The methodology used for the design and implementation of the DT includes (1) general principles and requirements; (2) models with functional views based on domains and entities; (3) attributes of the observable manufacturing elements; and (4) protocols for the exchange of information between entities. The results show that the integration of these technologies improves the monitoring, control, and simulation capabilities of processes using 3D resources and immersive environments, achieving a higher level of interactivity. In addition, error detection tests were carried out, where a reduction of time was observed in the resolution of errors that may be caused by internal or external disturbances of the process, thus avoiding production delays. [ABSTRACT FROM AUTHOR]

The article presents a strategy of the cyber-physical system guaranteed functioning for a small business enterprise (SBE), which is ensured by maintaining the digital twin and is due to its extremely high relevance in modern conditions. Business processes are linked to Industry 4.0 competencies. One of the innovations it implements is Digital Twin, a comprehensive facility support tool. Digital twin allows for tracking and effectively managing the entire cycle of an infrastructure project, from planning, procurement, and production to commissioning and maintenance of the facility. PEST, SWOT, SAW, TOPSIS, and VIKOR methods are used to build a strategy. [ABSTRACT FROM AUTHOR]

Digital Twin (DT) technology is far from being comprehensive and mature, resulting in their piecemeal implementation in practice where some functions are automated by DTs, and others are still performed by humans. This piecemeal implementation of DTs often leaves practitioners wondering what roles (or functions) to allocate to DTs in a work system, and how might it impact humans. A lack of knowledge about the roles that humans and DTs play in a work system can result in significant costs, misallocation of resources, unrealistic expectations from DTs, and strategic misalignments. To alleviate this challenge, this paper answers the research question: When humans work with DTs, what types of roles can a DT play, and to what extent can those roles be automated? Specifically, we propose a two-dimensional conceptual framework, Levels of Digital Twin (LoDT). The framework is an integration of the types of roles a DT can play, broadly categorized under (1) Observer, (2) Analyst, (3) Decision Maker, and (4) Action Executor, and the extent of automation for each of these roles, divided into five different levels ranging from completely manual to fully automated. A particular DT can play any number of roles at varying levels. The framework can help practitioners systematically plan DT deployments, clearly communicate goals and deliverables, and lay out a strategic vision. A case study illustrates the usefulness of the framework. • Digital Twin human interaction • Digital Twin can play multiple roles in a work system. • Levels of Digital Twin framework provides a common language for understanding. • Digital Twin must be evaluated for each case – a "one size fits all" does not work. [ABSTRACT FROM AUTHOR]

Ensuring a sustainable supply for humankind with mineral raw materials and preventing fuel and energy crises, minimizing human-made accidents and the negative impact of industry on the environment, the inflow of funds and innovations into the mining sector should be expanding in time and space. To do this, new mining platforms should have not only innovative and technological, but also social-and-economic coverage of the latest competencies, which Mining 4.0 fully corresponds to. The achievements of the Fourth Industrial Revolution, embodied in "end-to-end" digital and convergent technologies, are able to ensure the stable development of the mineral resource sector in the face of fluctuations in raw material demand and the profitability of mining enterprises, strengthening environmental safety legislation. Mining 4.0 is also a response to the technological shocks associated with the accelerated digital modernization of the manufacturing and infrastructure industries. This article attempts to give a multilateral overview of mining industries transformation in the course of the diffusion of Industry 4.0 technologies, to highlight the core and frontiers of Mining 4.0 expansion, to show the opportunities and threats of replacing physical systems and humans in mining with cyber-physical systems. Further, the technological, economic and social horizons of the transformation of Mining 4.0 into Mining 5.0 with specific threats of total digitalization are discussed. [ABSTRACT FROM AUTHOR]

The emergence of the fourth industrial revolution was made possible by the emergence and rapid spread of cyberphysical systems. One of the major developments of this era was the digital twin model. The digital twin is not just a 3D model or a simple simulation. It can be used to create a realtime model of the observed physical element that collects environmental data using sensors and can also predict the failure of a product based on the information provided. If the system has actuators, we are also able to feed back the change in the virtual model to the physical reality. Closely related to the modern digital model is the digital shadow, which collects data in the same way as the twin, but feedback to physical reality has not been implemented. In the dissertation, an initial concept is presented, which describes the application possibilities of digital shadow in terms of the procurement process of new technologies (stations, machines). [ABSTRACT FROM AUTHOR]

Modern society is living in an age of paradigm changes. In part, these changes have been driven by new technologies, which provide high performance computing capabilities that enable the creation of complex Artificial Intelligence systems. Those developments are allowing the emergence of new Cyber Systems where the continuously generated data is utilized to build Artificial Intelligence models used to perform specialized tasks within the system. While, on one hand, the isolated application of the cyber systems is becoming widespread, on the other hand, their synchronical integration with other cyber systems to build a concise and cognitive structure that can interact deeply and autonomously with a physical system is still a completely open question, only addressed in some works from a philosophical point of view. From this standpoint, the AI can play an enabling role to allow the existence of these cognitive CPSs. This review provides a look at some of the aspects that will be crucial in the development of cyber-physical systems, focusing on the application of artificial intelligence to confer cognition to the system. Topics such as control and optimization architectures and digital twins are presented as components of the CPS. It also provides a conceptual overview of the impacts that the application of these technologies might have in the chemical industry, more specifically in the purification of methane. [ABSTRACT FROM AUTHOR]

The 'Internet of Things' (IoT) is at times mythologized and its purpose mistaken, and often people can become confused about what it means, does or aims to achieve; moreover, without a financial appraisal of the differences IoT can make to their own enterprise, business leaders may be content to watch and wait rather than to take a lead. This issue of Textile Progress aims to provide a guide to help navigation beyond general statements about IoT and to help those involved with the textile industry to make an informed decision about its potential value to them. The definitions of the Internet of Things (IoT) available in the literature, and those put forth by organizations working on IoT standards development, are reviewed along with its architecture, elements, standards and protocols to help provide an understanding of the concepts and goals of IoT. A broad overview of impediments challenging the progress of IoT, especially in relation to cybersecurity, is provided. This review also compares ongoing work in the application of IoT in the textile industry to that in other manufacturing sectors whilst focussing on the interpretation of IoT technologies and their potential application to the textile industry according to both technological and business perspectives. A specific case study for the spinning industry is conducted to help with evaluation of its IoT solutions and to help to address how other parts of the textile industry might benefit from its application. The case study includes evaluation of IoT solutions in the spinning industry spanning the period between ITMA 2011 to 2019, and takes information from interviews with industry executives to inform future directions of IoT in the spinning industry. [ABSTRACT FROM AUTHOR]