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... Similarly, 3D virtual environments and discrete event simulation models are proposed for modeling, simulating, and evaluating manufacturing assets [31] in a virtual factory. In contrast, while a digital factory is likely to define its operational boundaries inside the company, a virtual factory extends the factory's capabilities across multiple organizations to provide a unified virtual environment to test, model, and simulate factory layouts and processes [32]. ...
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The technological advancements promote the rise of the fourth industrial revolution, where key terms are efficiency, innovation, and enterprises’ digitalization. Market globalization, product mass customization, and more complex products need to reflect on changing the actual design methods and developing business processes and methodologies that have to be data-driven, AI-assisted, smart, and service-oriented. Therefore, there is a great interest in experimenting with emerging technologies and evaluating how they impact the actual business processes. This paper reports a comparison among the major trends in the digitalization of a Factory of the Future, in conjunction with the two major strategic programs of Industry 4.0 and China 2025. We have focused on these two programs because we have had experience with them in the context of the FIRST H2020 project. European industrialists identify the radical change in the traditional manufacturing production process as the rise of Industry 4.0. Conversely, China mainland launched its strategic plan in China 2025 to promote smart manufacturing to digitalize traditional manufacturing processes. The main contribution of this review paper is to report about a study, conducted and part of the aforementioned FIRST project, which aimed to investigate major trends in applying for both programs in terms of technologies and their applications for the factory’s digitalization. In particular, our analysis consists of the comparison between Digital Factory, Virtual Factory, Smart Manufacturing, and Cloud Manufacturing. We analyzed their essential characteristics, the operational boundaries, the employed technologies, and the interoperability offered at each factory level for each paradigm. Based on this analysis, we report the building blocks in terms of essential technologies required to develop the next generation of a factory of the future, as well as some of the interoperability challenges at a different scale, for enabling inter-factories communications between heterogeneous entities.
Chapter
In Industry 4.0 manufacturing collaborative network, product design processes, manufacturing processes, maintenance processes should be integrated across different factories and enterprises. The collaborative manufacturing network 4.0 allows the amalgamation of manufacturing resources in multiple organizations to operate processes in a collaborative manner for reacting to the fast changes of markets or emergencies. In this paper, we propose a predictive maintenance service as a part of a virtual factory, a form of collaborative manufacturing network. Data-driven predictive maintenance service is built-in FIWARE, an industry 4.0 framework. To optimize predictive maintenance services based on different criteria within a virtual factor, such as geographical locations, similar types of machinery, or cost/time efficiency, etc., we provide our design and implementation to deal with providing better maintenance services and data exchanging across different collaborative partners with different requirements and modularizing of related functions.
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The recent advent of novel concepts and technologies, such as the Internet of Things (IoT), Big Data, Augmented Reality, Cloud Computing, and Artificial intelligence is transforming industry and society as a whole. Today, the large amount of data generated requires the design and development of new schemes for extracting valuable information. At the same time, The COVID-19 epidemic is posing unprecedented challenges for businesses, governments and companies around the world. This article refers to a company in Southern Italy that has decided to repurpose its production line to manufacture surgical masks. The situation is completely new for the firm, which does not have historical data. Therefore, the aim is to propose a FIWARE-based IoT architecture for supporting real-time data acquisition and enabling the creation of a digital twin. Preliminary results show that the proposed solution effectively helps the company in starting the new business. Furthermore, the use of digital twin-based real-time dashboards enables continuous and agile improvement in the management of warehouse, production and maintenance activities.
Chapter
The H2020 FIRST project addresses the virtual factories, which are digital abstractions of real factories. The exploitation of virtual factories enables interoperability between real components inside a factory as well as between different factories belonging to the same supply chain. Moreover, virtual factories can be exploited to manage and compose services inside a factory, defining dynamic adaptation of set of services depending on high-level goals. In this paper we sketch the project results and its current state.
Chapter
Industry 4.0 has shifted the manufacturing related processes from conventional processes within one organization to collaborative processes across different organizations. For example, product design processes, manufacturing processes, and maintenance processes across different factories and enterprises. This complex and competitive collaboration requires the underlying system architecture and platform to be flexible and extensible to support the demands of dynamic collaborations as well as advanced functionalities such as big data analytics. Both operation and condition of the production equipment are critical to the whole manufacturing process. Failures of any machine tools can easily have impact on the subsequent value-added processes of the collaboration. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related machineries using various analyses. In this context, this paper explores how the FIWARE framework supports predictive maintenance. Specifically, it looks at applying a data driven approach to the Long Short-Term Memory Network (LSTM) model for machine condition and remaining useful life to support predictive maintenance using FIWARE framework in a modular fashion.
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Nowadays, the introduction of digital technologies in the manufacturing industries paved the way to the Fourth Industrial Revolution, also known as Industry 4.0, fostering the evolution of traditional industrial systems to the concept of smart manufacturing. In last years, many reference models to describe the features of the smart factories have been proposed in literature. This paper aims at presenting an overview of these models in order to find the gaps in the research about the Industry 4.0 concepts. In particular, the business transformation required to evolve the traditional manufacturing systems into Industry 4.0-ready ones is discussed. Finally, future research topics are proposed.
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The global industrial landscape has changed deeply in the last few years due to successive technological developments and innovations in manufacturing processes. The Industry 4.0 concept has emerged and the academic literature has paid an increased attention to this topic, which remains non-consensual or ill defined. In this research, a literature review is made to understand this concept in its technological dimension, and to comprehend its impacts. This new industrial paradigm brings together the digital and physical worlds through the Cyber-Physical Systems enhanced by Internet of Things and it is expected that this novel has consequences on industry, markets and economy, improving production processes and increasing productivity, affecting the whole product lifecycle, creating new business models, changing the work environment and restructuring the labor market. Therefore, this paper focuses on Industry 4.0 concept and contributes for its clarification and further understanding about the importance and implications of this complex technological system.
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The vision of smart factory is based on the notion of Industry 4.0 that denotes technologies and concepts related to cyber-physical systems and the Internet of Things (IoT). In smart factories cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. Over the IoT, cyber-physical systems communicate and cooperate with each other in real time. This paper presents a smart factory architecture based on communication and computing layers that embed scheduling mechanisms within a mechanical shop floor. Every physical entity in the shop floor is seen as an autonomous intelligent agent that performs tasks guided by dynamic scheduling functions. A test bed has been set up to show how physical entities can be cooperative and autonomous units that can automatize the shop floor operation processes. The results verify the feasibility and efficiency of proposed method.
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Cloud manufacturing is a new concept extending and adopting the concept of Cloud computing for manufacturing. The aim is to transform manufacturing businesses to a new paradigm in that manufacturing capabilities and resources are componentized, integrated and optimized globally. This study presents an interoperable manufacturing perspective based on Cloud manufacturing. A literature search has been undertaken regarding Cloud architecture and technologies that can assist Cloud manufacturing. Manufacturing resources and capabilities are discussed in terms of Cloud service. A service-oriented, interoperable Cloud manufacturing system is proposed. Service methodologies are developed to support two types of Cloud users, i.e., customer user and enterprise user, along with standardized data models describing Cloud service and relevant features. Two case studies are undertaken to evaluate the proposed system. Cloud technology brings into manufacturing industry with a number of benefits such as openness, cost-efficiency, resource sharing and production scalability.
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Unexpected disturbances and local decisions almost always deteriorate the execution of manufacturing plans. Digital enterprise technologies are hard to use, due to the complexity of production and the frequently changing circumstances. One of the main goals of the research described in the paper is the automatic model building of the discrete-event simulation system, based on intelligent analysis of the huge amount of information incorporated in the production database. The developed solution supports shop-floor dispatching and shop-floor managers in making control decisions.
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Due to market pressures, manufacturing companies are forced to find new ways to remain competitive. Dynamic manufacturing networks (DMNs) could be a solution for this task. One of the main challenges during the process of building up and managing a DMN is the exchange of required data between the partners of such a DMN. However, the efficient exchange of relevant data between the partners within a DMN is a hard endeavour. Within this paper the authors show how a cloud-based integration platform for DMNs is able to effectively support the process of data exchange within a DMN. The major components of this platform and their functions are also discussed. Furthermore this paper presents how a cloud-based integration platform allows SMEs, even if they lack in proper production IT, to be part of a DMN that heavily relies on current production data.
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With the process of globalisation and the development of management models and information technology, enterprise cooperation and collaboration has developed from intra-enterprise integration, outsourcing and inter-enterprise integration, and supply chain management, to virtual enterprises and enterprise networks. Some midfielder enterprises begin to serve for different supply chains. Therefore, they combine related supply chains into a complex enterprise network. The main challenges for enterprise network’s integration and collaboration are business process and data fragmentation beyond organisational boundaries. This paper reviews the requirements of enterprise network’s integration and collaboration, as well as the development of new information technologies. Based on service-oriented architecture (SOA), collaboration modelling and collaboration agents are introduced to solve problems of collaborative management for service convergence under the condition of process and data fragmentation. A model-driven methodology is developed to design and deploy the integrating framework. An industrial experiment is designed and implemented to illustrate the usage of developed technologies in this paper.
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Smart Manufacturing is the dramatically intensified and pervasive application of networked information-based technologies throughout the manufacturing and supply chain enterprise. The defining technical threads are time, synchronization, integrated performance metrics and cyber-physical–workforce requirements. Smart Manufacturing responds and leads to a dramatic and fundamental business transformation to demand-dynamic economics keyed on customers, partners and the public; enterprise performance and variability management; real-time integrated computational materials engineering and rapid qualification, demand-driven supply chain services; and broad-based workforce involvement. IT-enabled Smart factories and supply networks can better respond to national interests and strategic imperatives and can revitalize the industrial sector by facilitating global competitiveness and exports, providing sustainable jobs, radically improving performance, and facilitating manufacturing innovation.
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This paper deals with the enterprise integration in the area of manufacturing operations and management. It aims at presenting an overview of an international standardization initiative carried out by the joint working group JWG15 of IEC and ISO. It is concerned with the development of a multipart standard, named IEC 62264: Enterprise-Control system integration. After a brief introduction on the background, goal and approach, some hierarchy models are reviewed, which are used as a basis to define the scope of the standard. Then, a functional data-flow model, object models and associated data attributes are outlined. The activity models of manufacturing operations management are presented. The focus of the paper is on the methodological approach to develop the standard and its underlying concepts. Future works and conclusions are given at the end of the paper.
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Rapid product/process realization and enterprise integration have been identified among the major imperatives for enabling the next generation manufacturing paradigm. This paper proposes a virtual factory modeling approach to support these imperatives. A virtual factory is defined as an integrated simulation model of major subsystems in a factory that considers the factory as a whole and provides an advanced decision support capability. It seeks to go beyond the typical modeling of one sub-system at a time, such as the manufacturing model, the business process model and/or the communication network model developed individually and in isolation. A basic virtual factory model of a semi-conductor backend factory has been developed for concept demonstration. Application examples are used to demonstrate the integration between business processes and manufacturing system performance. Future work will move further towards the development of the complete virtual factory and its industry applications.
Chapter
The Stuttgart Model of adaptive, transformable and virtual factories, already implemented in German basic research performed at the Universität Stuttgart has been extended with a new perspective, the so-called “Smart Factory”. The Smart Factory approach is a new dimension of multi-scale manufacturing by using the state-of-the-art ubiquitous/pervasive computing technologies and tools. The Smart Factory represents a context-sensitive manufacturing environment that can handle turbulences in real-time production using decentralized information and communication structures for an optimum management of production processes. This paper presents our research steps and future work in giving reality to the envisioned Smart Factory at the Universität Stuttgart.
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The comprehensive approach to the digital factory has become a subject of paramount importance to all major automotive companies, and the chances offered by it are numerous. However, extensive preliminary work is still necessary and thus requires a tremendous effort, especially for small and medium-sized enterprises (SME) which must be integrated as suppliers of components. The purposes of the present article are to describe, in a general way, how the vision of the digital factory can be implemented in reality and to outline the problems, which must still be expected in the further course of the endeavour. In addition, the status of research relating to the digital factory at IMAB, Anlagenprojektierung und Materialflusslogistik at the Technical University of Clausthal, and the fields of future developmental activity are illustrated through the use of an example.
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The paper defines and clarifies basic concepts of enterprise architectures. Then an overview on architectures for enterprise integration developed since the middle of the 1980s is presented. The main part of the paper focuses on the recent developments on architectures for enterprise interoperability. The main initiatives and existing works are presented. Future trends and some research issues are discussed and conclusions are given at the end of the paper.
BeinCPPS Architecture -Layers
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Tangible Industry 4.0: a scenariobased approach to learning for the future of production
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Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0 --Securing the Future of German Manufacturing Industry
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