385 research outputs found

    Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges

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    Cyber-Physical Systems (CPS) is an emergent approach that focuses on the integration of computational applications with physical devices, being designed as a network of interacting cyber and physical elements. CPS control and monitor real-world physical infrastructures and thus is starting having a high impact in industrial automation. As such design, implementation and operation of CPS and management of the resulting automation infrastructure is of key importance for the industry. In this work, an overview of key aspects of industrial CPS, their technologies and emerging directions, as well as challenges for their implementation is presented. Based on the hands-on experiences gathered from four European innovation projects over the last decade (i.e. SOCRADES, IMC-AESOP, GRACE and ARUM), a key challenges have been identified and a prioritization and timeline are pointed out with the aim to increase Technology Readiness Levels and lead to their usage in industrial automation environments.The authors would like to thank for their support the European Commission, and the partners of the EU FP6 SOCRADES (www.socrades.net), EU FP7 GRACE (www.grace-project.org), EU FP7 IMC-AESOP (www.imc-aesop.eu) and EU FP7 ARUM (www.arum-project.eu) projects, for their fruitful support and discussions.info:eu-repo/semantics/publishedVersio

    An agile and adaptive holonic architecture for manufacturing control

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    Tese de doutoramento. Engenharia Electrotécnica e de Computadores. 2004. Faculdade de Engenharia. Universidade do Port

    Cyber-physical Manufacturing in the Light of Professor Kanji Ueda's Legacy

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    Cyber-physical manufacturing, i.e., the formerly never seen integration of the physical and virtual worlds in the manufacturing domain is considered the substance of the 4th industrial revolution. Much of the changes deemed now revolutionary are originated in a long and converging progress of manufacturing science and technology, as well as of computer science, information and communication technologies. One of the pioneers and influential thinkers of production engineering who paved the way towards cyber-physical manufacturing was unquestionably Professor Kanji Ueda (1946-2015). With this paper the authors would like to pay a tribute to his achievements, by highlighting his main contributions not only to the advancement of production engineering and industrial technology but also to the sustainability of our society

    Life Cycle Engineering 4.0: A Proposal to Conceive Manufacturing Systems for Industry 4.0 Centred on the Human Factor (DfHFinI4.0)

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    Engineering 4.0 environments are characterised by the digitisation, virtualisation, and connectivity of products, processes, and facilities composed of reconfigurable and adaptive socio-technical cyber-physical manufacturing systems (SCMS), in which Operator 4.0 works in real time in VUCA (volatile, uncertain, complex and ambiguous) contexts and markets. This situation gives rise to the interest in developing a framework for the conception of SCMS that allows the integration of the human factor, management, training, and development of the competencies of Operator 4.0 as fundamental aspects of the aforementioned system. The present paper is focused on answering how to conceive the adaptive manufacturing systems of Industry 4.0 through the operation, growth, and development of human talent in VUCA contexts. With this objective, exploratory research is carried, out whose contribution is specified in a framework called Design for the Human Factor in Industry 4.0 (DfHFinI4.0). From among the conceptual frameworks employed therein, the connectivist paradigm, Ashby's law of requisite variety and Vigotsky's activity theory are taken into consideration, in order to enable the affective-cognitive and timeless integration of the human factor within the SCMS. DfHFinI4.0 can be integrated into the life cycle engineering of the enterprise reference architectures, thereby obtaining manufacturing systems for Industry 4.0 focused on the human factor. The suggested framework is illustrated as a case study for the Purdue Enterprise Reference Architecture (PERA) methodology, which transforms it into PERA 4.0

    A HOLISTIC APPROACH TO COMPUTER INTEGRATED MANUFACTURING ARCHITECTURE AND SYSTEMS DESIGN

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    This work addresses the problem of finding an improved solution to Computer Integrated Manufacturing (ClM) Architecture and Systems Design. The current approaches are shown to be difficult to understand and use, over complex. In spite of their complexity of approach they lack comprehensiveness and omit many factors and dimensions considered essential for success in today's competitive and often global market place. A new approach to ClM Architecture and Systems Design is presented which offers a simpler, more flexible and more robust format for defining a particular ClM System within a general architectural framework. At the same time this new approach is designed to offer a comprehensive and holistic solution. The research work involved the investigation of current approaches and research and development initiatives focusing particularly on the CIM-OSA and GRAI Integrated methodologies in the field of ClM Architecture. The strengths and weaknesses of the various approaches are examined. Developments in other related fields including manufacturing systems, manufacturing management, information technology and systems generally have been investigated regarding their relevance and possible contribution to an improved solution. The author has built on his practical experience in creating, designing and managing the implementation of a global CIM system. The authors work on several publicly funded collaborative research and development projects relevant to the problem area is described. These include CIM-OSA, IMOCIM and TIQS projects. In the latter two projects the author was instrumental in developing the methodological approach based on a systems approach to business processes in connection with the design of quality and manufacturing systems. Both of these projects have contributed to this work. The author has also participated in the global IMS programme as a rapporteur for the European Commission and this helped to provide a global perspective on the problems of manufacturing companies as they attempt to compete in a world wide market place. The results of this work provide the basis for a radically improved approach to ClM Architecture and Systems Design based on the holistic view of an enterprise. The approach developed supports the business process view of an enterprise; addresses the people and organisational aspects; leads to ClM solutions focused on meeting enterprise goals; and is able to deal with a significantly increased scope and complexity compared with existing methods yet is easily understood and more simple to simple to apply than current approaches

    Adapter module for self-learning production systems

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    Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica, Sistemas e ComputadoresThe dissertation presents the work done under the scope of the NP7 Self-Learning project regarding the design and development of the Adapter component as a foundation for the Self-Learning Production Systems (SLPS). This component is responsible to confer additional proprieties to production systems such as lifecycle learning, optimization of process parameters and, above all, adaptation to different production contexts. Therefore, the SLPS will be an evolvable system capable to self-adapt and learn in response to dynamic contextual changes in manufacturing production process in which it operates. The key assumption is that a deeper use of data mining and machine learning techniques to process the huge amount of data generated during the production activities will allow adaptation and enhancement of control and other manufacturing production activities such as energy use optimization and maintenance. In this scenario, the SLPS Adapter acts as a doer and is responsible for dynamically adapting the manufacturing production system parameters according to changing manufacturing production contexts and, most important, according to the history of the manufacturing production process acquired during SLPS run time.To do this, a Learning Module has been also developed and embedded into the SLPS Adapter. The SLPS Learning Module represents the processing unit of the SLPS Adapter and is responsible to deliver Self-learning capabilities relying on data mining and operator’s feedback to up-date the execution of adaptation and context extraction at run time. The designed and implemented SLPS Adapter architecture is assessed and validated into several application scenario provided by three industrial partners to assure industrial relevant self-learning production systems. Experimental results derived by the application of the SLPS prototype into real industrial environment are also presented

    Background, Systematic Review, Challenges and Outlook

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    Publisher Copyright: © 2013 IEEE. This research is supported by the Digital Manufacturing and Design Training Network (DiManD) project funded by the European Union through the Marie Skłodowska-Curie Innovative Training Networks (H2020-MSCA-ITN-2018) under grant agreement no. 814078The concept of smart manufacturing has attracted huge attention in the last years as an answer to the increasing complexity, heterogeneity, and dynamism of manufacturing ecosystems. This vision embraces the notion of autonomous and self-organized elements, capable of self-management and self-decision-making under a context-aware and intelligent infrastructure. While dealing with dynamic and uncertain environments, these solutions are also contributing to generating social impact and introducing sustainability into the industrial equation thanks to the development of task-specific resources that can be easily adapted, re-used, and shared. A lot of research under the context of self-organization in smart manufacturing has been produced in the last decade considering different methodologies and developed under different contexts. Most of these works are still in the conceptual or experimental stage and have been developed under different application scenarios. Thus, it is necessary to evaluate their design principles and potentiate their results. The objective of this paper is threefold. First, to introduce the main ideas behind self-organization in smart manufacturing. Then, through a systematic literature review, describe the current status in terms of technological and implementation details, mechanisms used, and some of the potential future research directions. Finally, the presentation of an outlook that summarizes the main results of this work and their interrelation to facilitate the development of self-organized manufacturing solutions. By providing a holistic overview of the field, we expect that this work can be used by academics and practitioners as a guide to generate awareness of possible requirements, industrial challenges, and opportunities that future self-organizing solutions can have towards a smart manufacturing transition.publishersversionpublishe

    Smart Agents in Industrial Cyber–Physical Systems

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    Smart Manufacturing and Intelligent Manufacturing:A Comparative Review

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    The application of intelligence to manufacturing has emerged as a compelling topic for researchers and industries around the world. However, different terminologies, namely smart manufacturing (SM) and intelligent manufacturing (IM), have been applied to what may be broadly characterized as a similar paradigm by some researchers and practitioners. While SM and IM are similar, they are not identical. From an evolutionary perspective, there has been little consideration on whether the definition, thought, connotation, and technical development of the concepts of SM or IM are consistent in the literature. To address this gap, the work performs a qualitative and quantitative investigation of research literature to systematically compare inherent differences of SM and IM and clarify the relationship between SM and IM. A bibliometric analysis of publication sources, annual publication numbers, keyword frequency, and top regions of research and development establishes the scope and trends of the currently presented research. Critical topics discussed include origin, definitions, evolutionary path, and key technologies of SM and IM. The implementation architecture, standards, and national focus are also discussed. In this work, a basis to understand SM and IM is provided, which is increasingly important because the trend to merge both terminologies rises in Industry 4.0 as intelligence is being rapidly applied to modern manufacturing and human–cyber–physical systems
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