Search

Big Data (BD)-driven business intelligence (BI) is considered to be a new development stage within industrial engineering management to achieve higher business value creation. The article provides extensive research of BD-driven approaches to identify strategic knowledge and value-creating components within Internet of Things (IoT) manufacturing. Due to previous research studies focused mostly on partial solutions and lack a holistic view, this article topic is still far from being sufficiently explored and resolved. The article presents the results of an extensive research study of contemporary professional literature with a specific aim to provide a more holistic and systematical view on the current state and future trends in this issue. To solve this high complex research problem, both quantitative and qualitative research methodology approaches are used. The article is primarily based on a systematic literature review covering 187 essential and 36 additional current research papers within the Web of Science and Scopus database from 2012 to the present. Research findings identify the seven key strategic elements determining that Big Data analytics (BDA) can become a real and tremendous business value creation driver within IoT intelligent manufacturing. These research results provide important conclusions and implications for challenging future investigations into BDA and BI systems in the context of advancing the Industry 4.0 revolution. In conclusion, we propose a final suggestion in the form of a strategic future research chain from IoT through BDA to value creation based on BD intelligence within IoT manufacturing, whereas seven strategic elements might play a crucial. Author, Internal Grant Agency of Tomas Bata University in Zlin [IGA/FaME/2020/009-Process]

Publisher Copyright: © 2013 IEEE. This work was supported in part by the European Union (EU) Project Advanced Manufacturing Solutions Tightly Aligned With Business Needs (AVANGARD), and in part by the European Union's Horizon 2020 Research and Innovation Program under Grant 869986. The scheduling applied to manufacturing represents a huge opportunity for companies to stand out in a world of fast and big changes. Having a reliable scheduling system will allow factories to deal with the significant demand for highly customized products. Although manufacturing scheduling has been deeply studied for decades, there is still a gap between academia and industry, namely because the lack of flexibility and homogeneity among scheduling solutions, which makes them very use case-oriented. Furthermore, the absence of standardization is also making it difficult to implement smart scheduling solutions in industrial scenarios. Thus, this work presents a set of requirements and design principles based on axiomatic design concept, to make the first steps to standardize the designing and development of manufacturing scheduling solutions in the context of Industry 4.0. At the end, is presented a scheduling generic framework targeting smart manufacturing and evaluated in a practical use case. publishersversion published

Cyber-physical production systems are constituted of various sub-systems in a production environment, from machines to logistics networks, that are connected and exchange data in real-time. Every sub-system consumes and generates data. This data has the potential to support decision making and optimization of production processes. To extract valuable information from this data, however, different data sources must be consolidated and analyzed. A Knowledge Graph (KG), also known as a semantic network, represents a net of real-world entities, i.e., machines, sensors, processes, or concepts, and illustrates their relationship. KG allows us to encode the knowledge and data context into a human interpretable form and is amenable to automated analysis and inference. This paper presents the potential of KG in manufacturing and proposes a framework for its implementation. The proposed framework should assist practitioners in integrating raw data from multiple data sources in production, developing a suitable data model, creating the knowledge graph, and using it in a graph application. Although the framework is applicable for different purposes, this work illustrates its use for supporting the quality assessment of products in a discrete manufacturing production line.

Continuous improvement of the fleet of technological equipment, in the conditions of existing production, is a difficult task, the solution of which must take into account many parameters, such as mechanization, automation and application of new technologies within the concept of Industry 4.0. One of the effective methods of improving the technical and economic indicators and reliability of production is the modernization of existing technological equipment, which is based on the need to use modern methods of development and implementation of information technology, such as Industrial Internet of Things, cyber-physical production systems. The subject of this study is the testing of methods for developing additive cyber design for automation of complex industrial equipment. The goal of this article is to develop a cyber-physical production control system for the modernization of the DA2238B hydraulic press. To achieve this goal it is necessary to solve the following tasks: to analyze the technical characteristics and control system of the hydraulic press DA2238B; to carry out research of the scheme of hydraulic basic and, taking it into account, to choose sensors and executive mechanisms, and also operating modes; to develop the scheme of inclusion of the hydro equipment; to develop an automated control system based on a modern single-board computer and to develop an intuitive additive cyber-design of the operator interface; to conduct experimental research. Conclusions: as a result of research, a block diagram was developed and an automated control system based on LattePanda was implemented. Selected pressure and temperature sensors for control of pressing processes, which allowed to ensure accurate compliance with the requirements of the technological process. An automated operator's workplace has been developed, in which touch control based on additive cyber-design has been implemented. The developed control system provides: collection and analysis of production data on the basis of which it is possible to improve production technology and to provide forecasting of achievement "Lean Manufacturing"., Постоянное совершенствование парка технологического оборудования, в условиях существующего производства, является сложной задачей, при решении которой необходимо учитывать много параметров, таких как механизация, автоматизация и применение новых технологий в рамках концепции Industry 4.0. Одним из эффективных методов повышения технико-экономических показателей и надежности производства является модернизация существующего технологического оснащения, которая основывается на необходимости использования современных методов разработки и внедрения информационных технологий, таких как Industrial Internet of Things, кибер-физических производственных систем. Предметом данного исследования является апробация методов разработки аддитивного кибер-дизайна для автоматизации сложного промышленного оборудования. Целью данной статьи является разработка кибер-физической производственной системы управления, для модернизации пресса гидравлического ДА2238Б. Для достижения поставленной цели необходимо решить следующие задачи: провести анализ технических характеристик и системы управления пресса гидравлического ДА2238Б; провести исследование схемы гидравлической принципиальной на базе которой выбрать датчики и исполнительные механизмы, а также режимы работы; разработать схему включения гидро- оборудования; разработать автоматизированную систему управления на базе современного одноплатного компьютера и разработать интуитивно понятный аддитивный кибер-дизайн интерфейса оператора; провести экспериментальные исследования. Выводы: в результате исследований была разработана структурная схема и реализована автоматизированная система управления на базе LattePanda. Выбраны датчики давления и температуры для контроля процессов прессования, что позволило обеспечить точное соответствие требованиям технологического процесса. Разработано автоматизированное рабочее место оператора, в котором реализовано сенсорное управление на базе аддитивного кибер-дизайна. Разработанная система управления обеспечивает: сбор и анализ производственных данных, на базе которых можно усовершенствовать технологию производства и обеспечить прогнозирование достижения "Lean Manufacturing"., Постійне вдосконалення парку технологічного обладнання, в умовах існуючого виробництва, є складним завданням, при вирішенні якого необхідно враховувати багато параметрів, таких як механізація, автоматизація і застосування нових технологій в рамках концепції Industry 4.0. Одним з ефективних методів підвищення техніко-економічних показників та надійності виробництва є модернізація існуючого технологічного оснащення, яка ґрунтується на необхідності використання сучасних методів розробки і впровадження інформаційних технологій, таких як Industrial Internet of Things, кібер-фізичних виробничих систем. Предметом даного дослідження є апробація методів розробки адитивного кібер-дизайну для автоматизації складного промислового обладнання. Метою даної статті є розробка кібер-фізичної виробничої системи керування, для модернізації преса гідравлічного ДА2238Б. Для досягнення поставленої мети необхідно вирішити такі завдання: провести аналіз технічних характеристик і системи керування преса гідравлічного ДА2238Б; провести дослідження схеми гідравлічної принципової на базі якої обрати датчики і виконавчі механізми, а також режими роботи; розробити схему включення гідро обладнання; розробити автоматизовану систему керування на базі сучасного одноплатного комп'ютера і розробити інтуїтивно зрозумілий адитивний кібер-дизайн інтерфейсу оператора; провести експериментальні дослідження. Висновки: в результаті досліджень було розроблено структурну схему та реалізовано автоматизовану систему керування на базі LattePanda. Обрані датчики тиску і температури для контролю процесів пресування, що дозволило забезпечити точне дотримання вимог технологічного процесу. Розроблено автоматизоване робоче місце оператора, в якому реалізовано сенсорне управління на базі адитивного кібер-дизайну. Розроблена система керування забезпечує: збір та аналіз виробничих даних, на базі яких можна вдосконалити технологію виробництва і забезпечити прогнозування досягнення "Lean Manufacturing".

With the concepts of Industry 4.0 and smart manufacturing gaining popularity, there is a growing notion that conventional manufacturing will witness a transition toward a new paradigm, targeting innovation, automation, better response to customer needs, and intelligent systems. Within this context, this review focuses on the concept of cyber–physical production system (CPPS) and presents a holistic perspective on the role of the CPPS in three key and essential drivers of this transformation: data-driven manufacturing, decentralized manufacturing, and integrated blockchains for data security. The paper aims to connect these three aspects of smart manufacturing and proposes that through the application of data-driven modeling, CPPS will aid in transforming manufacturing to become more intuitive and automated. In turn, automated manufacturing will pave the way for the decentralization of manufacturing. Layering blockchain technologies on top of CPPS will ensure the reliability and security of data sharing and integration across decentralized systems. Each of these claims is supported by relevant case studies recently published in the literature and from the industry; a brief on existing challenges and the way forward is also provided.

Anomaly detection is essential for realizing modern and secure cyber-physical production systems. By detecting anomalies, there is the possibility to recognize, react early, and in the best case, fix the anomaly to prevent the rise or the carryover of a failure throughout the entire manufacture. While current centralized methods demonstrate good detection abilities, they do not consider the limitations of industrial setups. To address all these constraints, in this study, we introduce an unsupervised, decentralized, and real-time process anomaly detection concept for cyber-physical production systems. We employ several 1D convolutional autoencoders in a sliding window approach to achieve adequate prediction performance and fulfill real-time requirements. To increase the flexibility and meet communication interface and processing constraints in typical cyber-physical production systems, we decentralize the execution of the anomaly detection into each separate cyber-physical system. The installation is fully automated, and no expert knowledge is needed to tackle data-driven limitations. The concept is evaluated in a real industrial cyber-physical production system. The test result confirms that the presented concept can be successfully applied to detect anomalies in all separate processes of each cyber-physical system. Therefore, the concept is promising for decentralized anomaly detection in cyber-physical production systems.

Production scheduling can be affected by many disturbances in the manufacturing system, and consequently, the feasible schedules previously defined became obsolete. Emerging of new technologies associated with Industry 4.0, such as Cyber-Physical Production Systems, as a paradigm of implementation of control and support in decision making, should embed the capacity to simulate different environment scenarios based on the data collected by the manufacturing systems. This paper presents the evaluation of environment dynamics effect on production scheduling, considering three scheduling models and three environment scenarios, through a case study. Results show that environment dynamics affect production schedules, and a very strong or strong positive correlation between environment dynamics scenarios and total completion time with delay, over three scheduling paradigms. Based on these results, the requirement for mandatory inclusion of a module for different environment dynamics scenarios generation and the corresponded simulations, of a Cyber-Physical Production Systems architecture, is confirmed., This work has been supported by FCT -Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.

Designing meat processing systems can be a complex process since this type of cyber-physical production system has two distinguishing properties: divergent product routings due to the cutting up of meat (known as co-production), and variability in the outcome of production processes (known as random yield). In this paper, we present a model-based design framework for meat processing systems, which can be used for performance prediction and system validation of a designed system. We show how an agent-based production control layer can be integrated into a model-based design architecture for meat processing systems. Finally, this framework describes how to route products through a system with co-production and random yield.

In recent years, the introduction of Industry 4.0 technologies in the manufacturing landscape promoted the development of smart factories characterised by relevant socio-technical interactions between humans and machines. In this context, understanding and modelling the role of humans turns out to be crucial to develop efficient manufacturing systems of the future. Grounding on previous researches in the field of Human-in-the-Loop and Human Cyber-Physical Systems, the paper aims at contributing to a deep reflection about human-machine interaction in the wider perspective of Social Human-in-the-Loop Cyber-Physical Production Systems, in which more agents collaborate and are socially connected. After presenting an evolution of manufacturing control organisations, an architecture to depict social interactions in smart factories is proposed. The proposed architecture contributes to the representation of different human roles in the smart factory and the exploration of both hierarchical and heterarchical data-driven decision-making processes in manufacturing.

With the inception of high-speed internet data services and ever-growing technical advancement in manufacturing technology, the integration of production systems and the internet of things to produce different types of jobs via cloud computing has become possible. The internet-enabled advanced automatic production systems can be referred to as the cyber-physical production systems (CPPS). The use of CPPS via cloud computing and the internet of things (IoT) can offer high productivity and high flexibility for the production of jobs in a dynamic production environment with varying specifications. The aim of this paper is to present a generalized synergic framework between different production facilities locating at different geographical locations to realize an energy-saving and efficient cyber-physical production system for the production of different types of jobs in the context of the industry 4.0 and beyond. In addition to the above, the study also identifies a need to address large scale multi-objective optimization issues to make the best decisions for different combinatorial production scenarios by using CPPS that are functioning in smart production facilities at different geographical locations.

Data-driven methods based on artificial intelligence (AI) are powerful yet flexible tools for gathering knowledge and automating complex tasks in many areas of science and practice. Despite the rapid development of the field, the existing potential of AI methods to solve recent industrial, corporate and social challenges has not yet been fully exploited. Research shows the insufficient practicality of AI in domain-specific contexts as one of the main application hurdles. Focusing on industrial demands, this publication introduces a new paradigm in terms of applicability of AI methods, called Usable AI (UAI). Aspects of easily accessible, domain-specific AI methods are derived, which address essential user-oriented AI services within the UAI paradigm: usability, suitability, integrability and interoperability. The relevance of UAI is clarified by describing challenges, hurdles and peculiarities of AI applications in the production area, whereby the following user roles have been abstracted: developers of cyber–physical production systems (CPPS), developers of processes and operators of processes. The analysis shows that target artifacts, motivation, knowledge horizon and challenges differ for the user roles. Therefore, UAI shall enable domain- and user-role-specific adaptation of affordances accompanied by adaptive support of vertical and horizontal integration across the domains and user roles.

Knowledge Graphs (KGs) have a large potential to support analytics and AI applications in the manufacturing domain, which in the age of Industry 4.0 is increasingly driven by data. Compared to other domains, however, KG techniques have seen limited adoption in this field so far. We argue that the construction of KGs in the context of Cyber-physical Production Systems (CPPSs) requires a systematic methodology grounded in domain-specific abstractions. Consequently, we introduce a KG modularization framework based on the well-established RAMI 4.0 architecture model. A key benefit of the proposed approach is that the resulting KGs support navigation across abstraction hierarchies, enabling bottom-up contextualization of raw data on the one hand, and top-down explanations by linking to lower levels of granularity on the other hand. We motivate the proposed approach and illustrate its application with a real-world use case from the automotive sector.

The complex workflows and interactions between heterogeneous entities in Cyber-Physical Production Systems (CPPS) call for the use of context-aware computing technology to operate effectively and meet the order requirements in a timely manner. In addition to the objective to meet the order due date, due to resource contention between production processes, CPPS may enter undesirable states. In undesirable states, all or part of the production activities are in waiting states or blocked situation due to improper allocation of resources. The capability to meet the order due date and prevent the system from entering an undesirable state poses challenges in the development of context-aware computing applications for CPPS. In this study, we formulate two situation awareness problems, including a Deadline Awareness Problem and a Future States Awareness Problem to address the above issues. In our previous study, we found that Discrete Timed Petri Nets provide an effective tool to model and analyze CPPS. In this paper, we present a relevant theory to support the operation of CPPS by extending the Discrete Timed Petri Nets to lay a foundation for developing context-aware applications of CPPS with deadline awareness and future states awareness capabilities. We illustrate the theory developed in this study by an example and conduct experiments to verify the computational feasibility of the proposed method.

Gemba Walks are an important mean of vertical integration in Lean Manufacturing environments. They ensure that all levels of the company stay connected with the front-line, “the Gemba”, where the actual value is created. However, traditionally Gemba Walks have been restricted to one location. This is a shortcoming in production environments characterized by interconnected and often globally dispersed problems where information from several locations is needed simultaneously. In response, this paper explores the emergence of new forms of Gemba Walks enabled by the adoption of new digital technologies. We intend to identify the advantages and disadvantages of using digital technology to support the execution of these new forms of Gemba Walks in more complex, globalized environments and to get a grasp of the extent to which digitalization changes communication characteristics between the parties involved.

Cyber-Physical Production Systems (CPPS) should adapt to new products or product variants efficiently and without extensive manual engineering effort. In comparison to rewriting the automation software for each adaption, manual engineering effort can be reduced by reusable software components with free parameters, which must be adjusted to individual production scenarios. This paper introduces CyberOpt Online, a novel online parameter estimation approach for reusable automation software components. In contrast to classic mathematical modeling approaches, such as Mixed Integer Nonlinear Programming (MINLP), our approach requires no predefined models that represent the system. Models, e. g., of the energy consumption of CPPS, are learned automatically from data observed during the operation of the production system. Therefore, the manual engineering effort is minimized as postulated by the paradigm of CPPS. The presented approach combines MINLP, process mining and black-box optimization techniques for calculating optimal timing parameter configurations for automation software components with free parameters in the domain of discrete manufacturing.

The main feature of cyber-physical production systems is its adaptability. They adapt quickly to new requirements such as new products or product variants. Nowadays, a bottleneck is the automation system, for which high manual engineering efforts are needed: Today, on-site technicians write and rewrite automation software, configure real-time communication protocols and create system configurations consisting of machine timing, physical dimensions of products, sensitivity, and motor control accelerations and velocities. Cyber-physical production systems often solve this dilemma by relying on reusable software components, which are composed in the overall automation software. However, this solution comes with a price, reusable software components need free parameters to adjust to the individual production configurations. This paper addresses this central research question and presents a novel parameter estimation approach to choose automatically optimal system configurations for cyber-physical production systems. Different scenarios from discrete manufacturing plants are used to evaluate the solution approach.

The autonomous and adaptable identification of anomalies in industrial contexts, particularly in the physical processes of Cyber-Physical Production Systems (CPPS), requires using critical technologies to identify failures correctly. Most of the existing solutions in the anomaly detection research area do not consider such systems’ dynamics. Due to the complexity and multidimensionality of CPPS, a scalable, adaptable, and rapid anomaly detection system is needed, considering the new design specifications of Industry 4.0 solutions. Immune-based models, such as the Dendritic Cell Algorithm (DCA), may provide a rich source of inspiration for detecting anomalies, since the anomaly detection problem in CPPS greatly resembles the functionality of the biological dendritic cells in defending the human body from hazardous pathogens. This paper tackles DCA limitations that may compromise its usage in anomaly detection applications, such as the manual characterization of safe and danger signals, data analysis not suitable for online classification, and the lack of an object-oriented implementation of the algorithm. The proposed approach, the Cursory Dendritic Cell Algorithm (CDCA), is a novel variation of the DCA, developed to be flexible and monitor physical industrial processes continually while detecting anomalies in an online fashion. This work’s contribution is threefold. First, it provides a comprehensive review of Artificial Immune Systems (AIS), focusing on AIS applied to the anomaly detection problem. Then, a new object-oriented architecture for the DCA implementation is described, enabling the modularity and abstraction of the algorithm stages into different classes (modules). Finally, the CDCA for the anomaly detection problem is proposed. The CDCA was successfully validated in two industrial-oriented dataset benchmarks for physical anomaly and network intrusion detection, the Skoltech Anomaly Benchmark (SKAB) and M2M using OPC UA. When compared to other algorithms, the proposed approach exhibits promising classification results. It was placed fourth on the SKAB scoreboard and presented a competitive performance with the incremental Dendritic Cell Algorithm (iDCA).

Industrial environments are heterogeneous systems that create challenges of interoperability limiting the development of systems capable of working collaboratively from the point of view of machines and software. Additionally, environmental issues related to manufacturing systems have emerged during the last decades, related to sustainability problems faced in the world. Thus, the proposed work aims to present an interoperable solution based on events to reduce the complexity of integration, while creating energetic profiles for the machines to allow the optimization of their energy consumption. A publish/subscribe-based architecture is proposed, where the instantiation is based on Apache Kafka. The proposed solution was implemented in two robotic cells in the automotive industry, constituted by different hardware, which allowed testing the integration of different components. The energy consumption data was then sent to a Postgres database where a graphical interface allowed the operator to monitor the performance of each cell regarding energy consumption. The results are promising due to the system’s ability to integrate tools from different vendors and different technologies. Furthermore, it allows the possibility to use these developments to deliver more sustainable systems using more advanced solutions, such as production scheduling, to reduce energy consumption.

Part 1: Lean Production; International audience; In this paper, the authors present practical insights in order to propose a Quality Management Framework for Digital Lean Manufacturing. The rationale behind this proposed framework is that the individual ISO 9000:2015 Quality Management pillars are rather different when it comes to their objectives and their tasks at hand when using Human capabilities and/or Digital technologies capabilities in a strategic way for (i) Quality Planning, (ii) Quality Control, (iii) Quality Assurance, and (iv) Quality Improvement in the context of the emerging paradigm of Digital Lean Manufacturing Systems.

International audience; The aim of this article is to overview the transition of smart logistic companies towards the Industry 4.0. Industry 4.0 is a generic concept that subscribes to the idea of a general awareness of the importance of new technologies for the manufacturing industry. This reflection aims to conserve and develop a strong and innovative industrial activity to support the modernization of different fields. In the case of high volumes service companies, many reasons have led to this modernization; remarkable increase in the volume of items to deliver, complexity of managing the transportation system and the personnel scheduling, humanization of work conditions, etc. In this work, The French postal service company, La Poste, is given as a particular case of study. La Poste daily treats high volumes of mail and parcel items. In this article, the organization of the French postal service system from the collect to the delivery is explicitly described. A literature review of different concerned research axes is presented. Finally, we present what we think are the future needs for researches in these axes.

The recent advances in technology and the demand for highly customized products have been forcing manufacturing companies to adapt and develop new solutions in order to become more dynamic and flexible to face the changing markets. Manufacturing scheduling plays a core role in this adaptation since it is crucial to ensure that all operations and processes are running on time in the factory. However, to develop robust scheduling solutions it is necessary to consider different requirements from the shopfloor, but it is not clear which constraints should be analyzed and most research studies end up considering very few of them. In this review article, several papers published in recent years were analyzed to understand how many and which requirements they consider when developing scheduling solutions for manufacturing systems. It is possible to understand that the majority of them are not able to be adapted to real systems since some core constraints are not even considered. Consequently, it is important to consider how manufacturing scheduling solutions can be structured to be adapted effortlessly for different manufacturing scenarios.

Biological collective systems have been an important source of inspiration for the design of production systems, due to their intrinsic characteristics. In this sense, several high level engineering design principles have been distilled and proposed on a wide number of reference system architectures for production systems. However, the application of bio-inspired concepts is often lost due to design and implementation choices or are simply used as heuristic approaches that solve specific hard optimization problems. This paper proposes a bio-inspired reference architecture for production systems, focused on highly dynamic environments, denominated BIO-inspired Self-Organising Architecture for Manufacturing (BIOSOARM). BIOSOARM aims to strictly adhere to bio-inspired principles. For this purpose, both shopfloor components and product parts are individualized and extended into the virtual environment as fully decoupled autonomous entities, where they interact and cooperate towards the emergence of a self-organising behaviour that leads to the emergence of the necessary production flows. BIOSOARM therefore introduces a fundamentally novel approach to production that decouples the system’s operation from eventual changes, uncertainty or even critical failures, while simultaneously ensures the performance levels and simplifies the deployment and reconfiguration procedures. BIOSOARM was tested into both flow-line and “job shop”-like scenarios to prove its applicability, robustness and performance, both under normal and highly dynamic conditions.

Rapid development in ICT and production engineering and the subsequent joining of both fields result currently in the creation of cyber-physical production systems. With these new technologies innovative didactic concepts are required to cope with new arising tasks on the job and in the development of such systems. Learning environments have proven to be effective instruments for developing competence in manufacturing training and education. To enable development of competence on cyber-physical production processes and systems a design approach for implementation in learning environments is presented. A case study illustrates the proposed implementation framework in a real learning factory.

This study aims to investigate how the use of cyber-physical production systems (CPPS) in small manufacturing firms can facilitate the implementation of the activity-based costing (ABC) method in calculating production costs, thereby increasing the reliability of costing information for decision-making purposes. The collaboration between the research team and managers allowed an in-depth knowledge of the manufacturing problems at the investigated firm, facilitating the analysis of the manufacturing process through both a costing and a managerial perspective. Findings show that the integration of CPPS investments with the ABC method, through real-time measurement of resources by sensors, apps and part programs, leads to useful information about operations, allowing firms to improve performance and reduce resource consumption. As a result, a model of integration between the factory control system, communication network and production system is proposed.

Ensuring uninterrupted interaction of modular industrial equipment units is one of the most important engineering tasks. The concept of Cyber-Physical Production Systems (CPPS) assumes that the distributed network should correspond to the current industrial process and be able to quickly reorganize it when changes occur. If composition of the equipment becomes more complicated, a standard topology with one central control node might get ineffective. This article describes the application of mesh-network technology to ensure the interaction of industrial devices and sensors included in the modular equipment. Virtual deployment of the network and a description network nodes interaction including new node registration in the dispatcher registry are given.

Cyber-physical Production Systems (CPPS) are one of the technical driving forces behind the transformation of industrial production towards "digital factory of the future" in the context of Industry 4.0. Security is a major concern for such systems as they become more intelligent, interconnected, and coupled with physical devices. For various security activities from security analysis to designing security controls and architecture, a systematic and structured view and presentation of security-related information is required. Based on the draft standard of Reference Architecture Model for Industry 4.0 (RAMI 4.0), we propose a practical approach to establish a security viewpoint in the CPPS reference architecture model. We investigate the feasibility of using an architecture modeling tool to implement the concept and leverage existing work on models of layered architecture. We demonstrate the applicability for security analysis in two example case studies.

Industrial competitiveness today means shorter product lifecycles, increased product variety and shorter time-tomarket. To face this challenge, the manufacturing industry is forced by different initiatives, namely Internet of Things, Industrie 4.0 and Cyber-Physical System to move from traditional control approaches towards intelligent manufacturing control systems that are dynamically adaptable to changing production environment and flexible to different processing tasks. For years several emergent intelligent approaches such as Multi-Agent Systems, Service-oriented Architecture, Plug-and- Produce systems and Cloud technologies have been developed in a variety of research fields. This paper reviews the state-of-theart methodologies and technologies of flexible and reconfigurable manufacturing systems outlining the differences between them and arising benefits within the vision of the PERFoRM (Production harmonizEd Reconfiguration of Flexible Robots and Machinery) project. Current trends and challenges in industrial implementation of these methodologies are reported and research opportunities are described.

Industrial competitiveness today means shorter product lifecycles, increased product variety and shorter time-to-market. To face this challenge, the manufacturing industry is forced by different initiatives, namely Internet of Things, Industrie 4.0 and Cyber-Physical System to move from traditional control approaches towards intelligent manufacturing control systems that are dynamically adaptable to changing production environment and flexible to different processing tasks. For years several emergent intelligent approaches such as Multi-Agent Systems, Service-oriented Architecture, Plug-and-Produce systems and Cloud technologies have been developed in a variety of research fields. This paper reviews the state-of-the-art methodologies and technologies of flexible and reconfigurable manufacturing systems outlining the differences between them and arising benefits within the vision of the PERFoRM (Production harmonizEd Reconfiguration of Flexible Robots and Machinery) project. Current trends and challenges in industrial implementation of these methodologies are reported and research opportunities are described.

[ES] Es evidente que en los próximos años gran parte de las tecnologías recogidas bajo el marco de la denominada Industria 4.0 tendrá un profundo impacto en todas las empresas y entre ellas, en las relacionadas con la explotación y producción de petróleo y gas. La automatización de bajo coste promueve arquitecturas de referencia rentables y nuevos enfoques de desarrollo para aumentar la flexibilidad y la eficiencia de las operaciones de producción en una planta industrial. En este sentido, OPC UA, proporciona acceso local y remoto a la información de planta, facilitando un mecanismo reconocido de integración tanto horizontal como vertical de manera correcta, segura y eficiente. El objetivo principal de este artículo es presentar una arquitectura abierta para la integración vertical basada en sistemas ciber-físicos de producción, configurados bajo la norma IEC 61499 y usando OPC UA, apta para su utilización en la fabricación flexible en la industria de petróleo ygas., [EN] It is clear that in the next few years most of the technologies involved in the so-called Industry 4.0 will have a deep impact on manufacturing companies, including those related to Oil & Gas exploration and production. Low cost automation promotes reference architectures and development approaches aiming at increasing the flexibility and efficiency of production operations in industrial plants. In this sense, OPC UA, in addition to allowing companies to join the Industry 4.0 initiative, provides local and remote access to plant information, enabling a recognized mechanism for both, horizontal and vertical integration in a reliable, safe and efficient way. The contribution of this article is an open architecture for vertical integration based on cyber-physical production systems, configured under IEC 61499 and using OPC UA, suitable to achieve flexible manufacturing within Oil & Gas industry., Este trabajo ha sido financiado por el MINECO/FEDER, UE del Gobierno de España bajo el proyecto DPI2015-68602-R y por el Gobierno Vasco/EJ bajo el reconocimiento de grupo de investigación IT914-16. Así mismo como al Gobierno Ecuatoriano a través de la Beca SENESCYT “Convocatoria abierta 2013”.

Emerging concepts such as Smart Production, Industrial Internet of Things, and Industry 4.0 bring a radically new set of requirements to the way industrial systems are engineered. In what concerns the communication infrastructure, support to dynamic environments, interoperability and heterogeneity, combined with a significant increase in the number of devices, are just a few of the challenges that must be faced. Software-defined networking (SDN) is a disruptive networking paradigm that emerged on campus networks but soon captured considerable interest from industry and academia, and is considered a leap forward in open traffic management. It exhibits two particular features that are very well suited to manage a network with real-time requirements: (i) a centralized resource control, completely decoupled from the data plane, and (ii), a fine granular resource control, down to validating each single frame received in each port of a switch. However, due to its roots, the SDN traffic model favors the average network throughput with best-effort policies, eventually imposing bandwidth constraints or setting fixed priorities. This model is not compatible with industrial scenarios, which typically have strict requirements in terms of predictability, timeliness and fault tolerance. This dissertation supports the thesis that is possible to support the flexibility, timeliness, and heterogeneity requirements of emerging industrial applications by enhancing SDN technologies with the means to enact resource reservations on networks comprising switching platforms with component-based real-time services. In particular, this work: (i) enhances a real-time switching platform with SDN services, (ii) develops extensions to OpenFlow, a seminal SDN protocol, so an OpenFlow controller may be able to configure both SDN and real-time services, and (iii) extends an OpenFlow controller with scheduling analysis so it may perform the admission control of new traffic flows while maintaining the timeliness behavior of the whole network. A prototype of the proposed real-time SDN framework is used to perform several experiments that validate the desired properties and consequently, the thesis. Conceitos emergentes como Produção Inteligente, Internet Industrial das Coisas e Indústria 4.0 trazem um conjunto radicalmente novo de requisitos para o modo como os sistemas industriais são projetados. No que diz respeito à infra-estrutura de comunicação, o suporte a ambientes dinâmicos, interoperabilidade e heterogeneidade, combinado com um aumento significativo no número de dispositivos, são apenas alguns dos desafios que devem ser enfrentados. Redes definidas por software (SDN) é um paradigma de rede disruptivo que surgiu nas redes de campus, mas logo conquistou um interesse considerável da indústria e da academia, e é considerado um salto na gestão de tráfego aberto. Ele exibe dois recursos específicos que são muito adequados para gerir uma rede com requisitos em tempo real: (i) um controlo de recursos centralizado, completamente desacoplado do plano de dados, e (ii), um controlo granular de recursos, trama a trama. No entanto, devido às suas raízes, o modelo de tráfego SDN favorece o rendimento médio da rede com políticas de melhor esforço, eventualmente impondo restrições de largura de banda ou definindo prioridades fixas. Este modelo não é compatível com cenários industriais, que normalmente têm requisitos rigorosos em termos de previsibilidade, pontualidade e tolerância a falhas. Esta dissertação suporta a tese de que é possível suportar a flexibilidade, a pontualidade e os requisitos de heterogeneidade de aplicações industriais emergentes, aprimorando as tecnologias SDN com os meios para aprovar reservas de recursos em redes que incluem plataformas de comutação com serviços em tempo real baseados em componentes. Em particular, este trabalho: (i) aprimora uma plataforma de comutação em tempo real com serviços SDN, (ii) desenvolve extensões para o OpenFlow, um protocolo seminal SDN, para que um controlador OpenFlow possa configurar serviços SDN e em tempo real, e (iii) estende um controlador OpenFlow com análise de escalonamento para que possa executar o controle de admissão de novos fluxos de tráfego enquanto mantém o comportamento de prontidão de toda a rede. Um protótipo da estrutura SDN em tempo real proposto é usado para executar vários experimentos que validam as propriedades desejadas e, consequentemente, a tese. Programa Doutoral em Telecomunicações