Cloud Manufacturing


Cloud Manufacturing is a service-oriented, high efficiency and low consumption, knowledge-based new mode of networked manufacturing.  It aims at facilitating manufacturers to minimize their product life-cycle expenses, maximize production efficiency, and provide agile accommodation of available manufacturing assets to the variable demand of customers.  Cloud Manufacturing assumes that manufacturers use crowdsourcing and outsourcing models for manufacturing operations and supporting services . Cloud Manufacturing principles allow flexible scaling of manufacturing assets  and instant access to the most efficient, innovative business technology solutions on a pay-as-you-go basis . Cloud Manufacturing applies well-known basic concepts from the field of Cloud Computing and the Internet of Things to the manufacturing domain. 

The purpose of Cloud Manufacturing is to move from production-oriented manufacturing processes to service-oriented manufacturing process networks by modelling single manufacturing assets as services in a similar vein as Software-as-a-Service or Platform-as-a-Service solutions are already provided by Cloud providers. By mapping well-known concepts from the field of Cloud computing to real-world manufacturing processes, Cloud Manufacturing takes service-oriented manufacturing processes to the next level and supports these processes by cloud-based software and IT infrastructure.  To achieve the formulated purpose, the following principles are applied:

  • Leasing and releasing manufacturing assets in an on-demand, utility-like fashion,
  • Rapid elasticity through scaling leased assets up and down if necessary, and
  • Pay-per-use through metered service.

Cloud Manufacturing Scenario. A manufacturing process is a set of process steps to be executed in order to create a certain valued product. In reality, these process steps are single services representing specific manufacturing assets and activities on the shop floor. The means that are offered by Cloud Manufacturing are intended to integrate single services of the manufacturing processes from distributed locations as if the complete manufacturing was carried out on the same shop floor (see Fig. 1). To achieve this, manufacturers need to virtualise their manufacturing assets into single software services. An integration of those services is possible via a Cloud Manufacturing platform, where these services are presented, advertised, leased, and sold as a part of manufacturing processes maintained in the platform. 

As an example manufacturing process, we consider the manufacturing of a generic product, which is assembled by a Manufacturing Company in four steps: (1) some needed composite parts are produced by Supplier A, (2) some auxiliary parts are produced by Supplier B, (3) the product is assembled on the shop floor of the Manufacturing Company's own plant, and (4) the finished product is verified according to quality aspects. Each of these single steps of the considered manufacturing process can be encapsulated as a service. The process model of this manufacturing process is created by means of Business Process Model Notation (BPMN) by adding services as process steps into the model. When a process model is foreseen for enactment, a process instance is created. When a process task (i.e., step) must be enacted, a corresponding service is deployed onto appropriate Cloud-based computational resources and executed accordingly.

The challenge here is that a large amount of interdependent processes with different Quality of Service (QoS) and Service Level Agreements (SLA) aspects may be requested at any point of time. Therefore, elastic processes are a promising approach in Cloud Manufacturing


                                                           Figure 1: High-level Cloud Manufacturing Scenario

Manufacturing Assets. Manufacturing assets consist of diversified and distributed manufacturing resources (equipment, materials, software, knowledge, and skills) and manufacturing capabilities (design, production, experimentation, management, and communication). Manufacturing assets are assigned to the users on demand . For this, three principal actions to be applied:

  • Creating models for resource data: Encapsulation of the real-world manufacturing assets and services as software entities executable in the cloud,
  • Grouping and aggregating of manufacturing assets into virtualised assets, and
  • Performing the description of the virtualised assets as Cloud services that can be used by all users in a Cloud Manufacturing platform.

These actions embrace a provision of a service description (using ontologies and semantic descriptions) including a clear identifier of the service, the definition of related data sources (for monitoring purposes), and the upload of the software service to the Marketplace of the Cloud Manufacturing platform, which is a central place where users are able to market their manufacturing assets and services in a Cloud-based way. Management of the Cloud services (dynamic location, monitoring, allocation, and reconfiguration) has to be provided and a pool of logical resources which are then used by the users has to be established in the Cloud Manufacturing platform by means of interfaces. 

Manufacturing Asset Virtualisation. A key characteristic of virtualisation is a quality of a manufacturing service and the efficiency of service encapsulation. First, an identification of manufacturing resources has to be performed. Manufacturing resource information then has to be virtualised and monitored in real-time The main problems to be addressed by a Cloud Manufacturing platform are the heterogeneity of Cloud Manufacturing assets: multi-domain, multi-level and multi-granularity ; manifoldness of QoS information; and selection of sharing strategies for manufacturing assets . These problems are solved by application of service virtualisation methods and tools that combine manufacturing resource virtualisation and manufacturing capability servitization. The process of servitization implies incorporation by manufacturers of the services into their product proposals or even substitution of the products by services. Comparing to virtualisation in Cloud computing, Cloud Manufacturing virtualisation addresses a problem of establishing a comprehensive data model for representing manufacturing assets. Notably the meaning of manufacturing services differs from the one of traditional Web services, since Web service standards have no means to represent heterogeneous characteristics of manufacturing resources .

Relationship to CREMA

CREMA is a Cloud Manufacturing platform that aims to establish manufacturing collaboration and facilitate stakeholfer interaction, to provide means to virtualise manufacturing services and create manufacturing processes, and to enact processes effectively by performing design time and runtime optimisation.


  1. L. Mazzola, P. Kapahnke, P. Waibel, C. Hochreiner, M. Klusch, "FCE4BPMN: On-demand QoS-based Optimised Process Model Execution in the Cloud", in Proc. 23rd IEEE International Technology Management Conference (ITMC), IEEE, 2017
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  2. O. Skarlat et al., "Towards a Methodology and Instrumentation Toolset for Cloud Manufacturing," International Workshop on Cyber-Physical Production Systems at CPS Week 2016, Vienna, Austria, IEEE, 2016, pp. 1-4. Link
    Cloud Manufacturing is a recent concept to realize real-world manufacturing processes by applying a combination of well-known principles from the fields of Cloud Computing, Business Process Management (BPM), and Internet of Things (IoT) to the manufacturing domain. Cloud Manufacturing assumes using crowdsourcing and outsourcing types of business mobilization to transform local production-oriented manufacturing into global service-oriented manufacturing networks. While there have been a number of conceptual frameworks for Cloud Manufacturing, there is still a lack of concrete methodologies and instrumentation. Especially, the integration of generic Cyber-Physical Systems (CPS) shop floors and the support of manufacturing business processes in the Cloud have not been given full consideration yet. Within this paper, we intend to define a research agenda and according missing methodologies and instrumentation to ground a platform for Cloud Manufacturing.
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  3. O. Skarlat et al. "Resource Provisioning for IoT Services in the Fog," in 9th IEEE International Conference on Service Oriented Computing and Applications (SOCA 2016), Macau, China, IEEE, 2016, pp. 32-39. Link
    The advent of the Internet of Things (IoT) leads to the pervasion of business and private spaces with ubiquitous, networked computing devices. These devices do not simply act as sensors, but feature computational, storage, and networking resources. These resources are close to the edge of the network, and it is a promising approach to exploit them in order to execute IoT services. This concept is known as fog computing. Despite existing theoretical foundations, the adoption of fog computing is still at its very beginning. Especially, there is a lack of approaches for the leasing and releasing of resources. To resolve this shortcoming, we present a conceptual framework for fog resource provisioning. We formalize an optimization problem which is able to take into account existing resources in fog/IoT landscapes. The goal of this optimization problem is to provide delay-sensitive utilization of available fog-based computational resources. We evaluate the resource provisioning model to show the benefits of our contributions. Our results show a decrease in delays of up to 39% compared to a baseline approach, yielding shorter round-trip times and makespans.
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  4. R. F. Babiceanu, and R. Seker, "Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook," Journal of Computers in Industry, vol. 81, pp. 128-137, 2016. Link
    The recent advances in sensor and communication technologies can provide the foundations for linking the physical manufacturing facility and machine world to the cyber world of Internet applications. The coupled manufacturing cyber-physical system is envisioned to handle the actual operations in the physical world while simultaneously monitor them in the cyber world with the help of advanced data processing and simulation models at both the manufacturing process and system operational levels. Moreover, a sensor-packed manufacturing system in which each process or piece of equipment makes available event and status information, coupled with market research for true advanced Big Data analytics, seem to be the right ingredients for event response selection and operation virtualization. As a drawback, the resulting manufacturing cyber-physical system will be vulnerable to the inevitable cyber-attacks, unfortunately, so common for the software and Internet-based systems. This reality makes cybersecurity penetration within the manufacturing domain a need that goes uncontested across researchers and practitioners. This work provides a review of the current status of virtualization and cloud-based services for manufacturing systems and of the use of Big Data analytics for planning and control of manufacturing operations. Building on already developed cloud business solutions, cloud manufacturing is expected to offer improved enterprise manufacturing and business decision support. Based on the current state-of-the-art cloud manufacturing solutions and Big Data applications, this work also proposes a framework for the development of predictive manufacturing cyber-physical systems that include capabilities for attaching to the Internet of Things, and capabilities for complex event processing and Big Data algorithmic analytics.
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  5. O. Skarlat, "Elastic Manufacturing Process Landscapes," in 8th ZEUS Workshop (ZEUS 2016), Vienna, Austria, 2016, pp. 55-48. Link
    Because of increasing competition and cost pressure, the manufacturing industry is currently undergoing massive changes that are facilitated by the usage of Information Technologies. Two particular aspects are the usage of Business Process Management (BPM) and Cloud technologies and concepts in the manufacturing domain. Rapid elasticity is crucial for the enactment of manufacturing processes in the Cloud. This work in progress paper aims at presenting some basic principles of elastic processes in the manufacturing domain. Henceforth, an approach towards adaptive infrastructure provisioning that allows for predefined Quality of Service (QoS) and Service Level Agreement (SLA) metrics in manufacturing Cloud environments is considered.
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  6. Y. Zhang et al., "Research on services encapsulation and virtualization access model of machine for cloud manufacturing," Journal of Intelligent Manufacturing, pp. 1-15, 2015. Link
    Considering the new requirements of the services encapsulation and virtualization access of manufacturing resources for cloud manufacturing (CMfg), this paper presents a services encapsulation and virtualization access model for manufacturing machine by combining the Internet of Things techniques and cloud computing. Based on this model, some key enabling technologies, such as configuration of sensors, active sensing of real-time manufacturing information, services encapsulation, registration and publishing method are designed. By implementing the proposed services encapsulation and virtualization access model to manufacturing machine, the capability of the machine could be actively perceived, the production process is transparent and can be timely visited, and the virtualized machine could be accessed to CMfg platform through a loose coupling, ‘plug and play’ manner. The proposed model and methods will provide the real-time, accurate, value-added and useful manufacturing information for optimal configuration and scheduling of large-scale manufacturing resources in a CMfg environment.
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  7. Y. Lu et al., "Development of a Hybrid Manufacturing Cloud," Journal of Manufacturing Systems, vol. 33, no. 4, pp. 551–566, 2014. Link
    Cloud manufacturing is emerging as a novel business paradigm for the manufacturing industry, in which dynamically scalable and virtualised resources are provided as consumable services over the Internet. A handful of cloud manufacturing systems are proposed for different business scenarios, most of which fall into one of three deployment modes, i.e. private cloud, community cloud, and public cloud. One of the challenges in the existing solutions is that few of them are capable of adapting to changes in the business environment. In fact, different companies may have different cloud requirements in different business situations; even a company at different business stages may need different cloud modes. Nevertheless, there is limited support on migrating to different cloud modes in existing solutions. This paper proposes a Hybrid Manufacturing Cloud that allows companies to deploy different cloud modes for their periodic business goals. Three typical cloud modes, i.e. private cloud, community cloud and public cloud are supported in the system. Furthermore, it enables companies to set self-defined access rules for each resource so that unauthorised companies will not have access to the resource. This self-managed mechanism gives companies full control of their businesses and boosts their trust with enhanced privacy protection. A unified ontology is developed to enhance semantic interoperability throughout the whole process of service provision in the clouds. A Cloud Management Engine is developed to manage all the user-defined clouds, in which Semantic Web technologies are used as the main toolkit. The feasibility of this approach is verified through a group of companies, each of which has complex access requirements for their resources. In addition, a use case is carried out between customers and service providers. This way, optimal service is delivered through the proposed system.
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  8. S. Schulte et al., "Towards Process Support for Cloud Manufacturing," 18th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2014), Ulm, Germany, 2014, pp. 142–149. Link
    Due to increasing competitive pressure, manufacturing companies need to support flexible and scalable business processes - both on the shop floor and in their enterprise software systems. Cloud manufacturing is a recent approach to realize real-world manufacturing processes by applying well-known basic concepts from the field of Cloud computing to this domain. To implement Cloud manufacturing, it is necessary to model, enact and monitor according manufacturing processes and virtualize the single process steps. So far, Business Process Management Systems do not explicitly support Cloud manufacturing. This paper analyzes requirements regarding process enactment for Cloud manufacturing and provides a concept for an according software framework.
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  9. X. Wen and X. Zhou, "Servitization of manufacturing industries based on cloud-based business model and the down-to-earth implementary path," Journal of Advanced Manufacturing Technology, vol. 87, no. 5, pp. 1491-1508, 2014. Link
    Servitization of manufacturing industries (SMI) is emerging as a new trend all around the world, especially in major manufacturing nations, e.g., USA, Germany, and China, which is believed to be able to upgrade the industry chains and improve the economy. Almost simultaneously, cloud manufacturing (CM) is being generally debated within the academic field as a new manufacturing paradigm. However, little attention was paid to implementing SMI based on cloud-based business model. This paper aims to discuss the ways to make SMI possible through cloud-based manufacturing business model (CMBM) and relevant enabling technologies including CM. Specifically, this paper (1) presents the key elements that motivate SMI and the full definition of SMI; (2) proposes a description of CMBM matching up with the execution of SMI; (3) provides some constructive advices, namely the down-to-earth path, with technology aspects included for the government and enterprises to carry out SMI based on CMBM; and (4) offers a case study related to successful SMI based on CMBM.
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  10. F. Tao et al., "CCIoT-CMfg: Cloud Computing and Internet of Things-Based Cloud Manufacturing Service System," IEEE Transactions on Industrial Informatics vol. 10, pp. 1435–1442, 2014. Link
    Recently, Internet of Things (IoT) and cloud computing (CC) have been widely studied and applied in many fields, as they can provide a new method for intelligent perception and connection from M2M (including man-to-man, man-to-machine, and machine-tomachine), and on-demand use and efficient sharing of resources, respectively. In order to realize the full sharing, free circulation, on-demand use, and optimal allocation of various manufacturing resources and capabilities, the applications of the technologies of IoT and CC in manufacturing are investigated in this paper first. Then, a CC- and IoT-based cloud manufacturing (CMfg) service system (i.e., CCIoT-CMfg) and its architecture are proposed, and the relationship among CMfg, IoT, and CC is analyzed. The technology system for realizing the CCIoT-CMfg is established. Finally, the advantages, challenges, and future works for the application and implementation of CCIoT-CMfg are discussed.
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  11. L. Zhang et al., "Cloud manufacturing: a new manufacturing paradigm," Journal of Enterprise Information Systems, vol. 8, no. 2, pp. 1–21, 2014. Link
    Combining with the emerged technologies such as cloud computing, the Internet of things, service-oriented technologies and high performance computing, a new manufacturing paradigm – cloud manufacturing (CMfg) – for solving the bottlenecks in the informatisation development and manufacturing applications is introduced. The concept of CMfg, including its architecture, typical characteristics and the key technologies for implementing a CMfg service platform, is discussed. Three core components for constructing a CMfg system, i.e. CMfg resources, manufacturing cloud service and manufacturing cloud are studied, and the constructing method for manufacturing cloud is investigated. Finally, a prototype of CMfg and the existing related works conducted by the authors' group on CMfg are briefly presented.
  12. N. Liu et al., "Multi-granularity resource virtualization and sharing strategies in cloud manufacturing," Journal of Network and Computer Applications, vol. 46, pp. 72–82, 2014. Link
    Cloud Manufacturing is a new and promising manufacturing paradigm. Resource virtualization is critical for Cloud Manufacturing. It encapsulates physical resources into cloud services and determines the robustness of the cloud platform. This paper proposes novel multi-granularity resource virtualization and sharing strategies for bridging the gap between complex manufacturing tasks and underlying resources. The proposed approach considers three factors, including workflow, activity, and resource that significantly influence stepwise decompositions of a complex manufacturing task. Resource aggregation functions are constructed to classify resources over different granularities. Resource clustering algorithms are presented for mapping physical resources to virtualized resources. Cloud service specifications are designed to describe virtualized resources and are implemented through a prototype. A case study is illustrated to validate the proposed approach.
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  13. D. Wu et al., "Cloud Manufacturing: Strategic vision and state-of-the-art," Journal of Manufacturing Systems, vol. 32, no. 4, pp. 564–579, 2013. Link
    Cloud manufacturing, a service oriented, customer centric, demand driven manufacturing model is explored in both its possible future and current states. A unique strategic vision for the field is documented, and the current state of technology is presented from both industry and academic viewpoints. Key commercial implementations are presented, along with the state of research in fields critical to enablement of cloud manufacturing, including but not limited to automation, industrial control systems, service composition, flexibility, business models, and proposed implementation models and architectures. Comparison of the strategic vision and current state leads to suggestions for future work, including research in the areas of high speed, long distance industrial control systems, flexibility enablement, business models, cloud computing applications in manufacturing, and prominent implementation architectures.
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  14. T. Rahmani et al., "Ontology-based integration of heterogenoeus material data resources in Product Lifecycle Management," IEEE International Conference on Systems, Man, and Cybernetics SMC, 2013, pp. 4589–4594. Link
    In the context of Design for Environment (DfE) in product development, material selection is particularly important for the product. It affects its design, its manufacturing process, and the environmental impacts over its life cycle phases [1]. For technical or functional reasons, specific materials with best fitting characteristics are needed. Yet, their extraction and manufacturing process causes a substantial negative environmental impact. Material information are split or hidden in many different sources inside a company within various departments and also outside, e.g. involving information from suppliers and standardization organizations. The granularity of material information, various old and new norms, varying names for the same material, and different languages pose a problem, especially international cooperation. This paper describes a research approach for an ontology-based integration of heterogeneous material data resources from different stages of the product lifecycle.
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  15. D. Wu et al., "Cloud Manufacturing: drivers, current status, and future trends," International Manufacturing Science and Engineering Conference ASME, Madison, Wisconsin, USA, 2013. Link
    Cloud Manufacturing (CM) refers to a customer-centric manufacturing model that exploits on-demand access to a shared collection of diversified and distributed manufacturing resources to form temporary, reconfigurable production lines which enhance efficiency, reduce product lifecycle costs, and allow for optimal resource loading in response to variable-demand customer generated tasking. Our objective is to present the drivers, current status of research and development, and future trends of CM. We also discuss the potential short term and long term impacts of CM on various sectors.
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  16. N. Liu, and X. Li, "A Resource Virtualization Mechanism for Cloud Manufacturing Systems," 4th International IFIP Working Conference, Harbin, China, 2012, vol. 122, pp. 46–59. Link
    Virtualization is critical for resource sharing and dynamic allocation in cloud manufacturing, a new service-oriented networked collaborative manufacturing model. In this paper, an effective method is proposed for manufacturing resources & capabilities virtualization, which contains manufacturing resources modeling and manufacturing cloud services encapsulation. A manufacturing resource virtual description model is built, which includes both nonfunctional and functional features of manufacturing resources. The model provides a comprehensive manufacturing resource view and information for various manufacturing applications. The OWL-S is adapted to an upper level ontology model, according to which manufacturing resources & capabilities are encapsulated into manufacturing cloud service. The proposed method is applied to the virtualization process of an aerospace company. Effectiveness and efficiency are illustrated for the manufacturing cloud services discovery and management.
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  17. X. Xu, "From cloud computing to cloud manufacturing," Journal of Robotics and Computer-Integrated Manufacturing, vol. 28, no. 1, pp. 75–86, 2012. Link
    Cloud computing is changing the way industries and enterprises do their businesses in that dynamically scalable and virtualized resources are provided as a service over the Internet. This model creates a brand new opportunity for enterprises. In this paper, some of the essential features of cloud computing are briefly discussed with regard to the end-users, enterprises that use the cloud as a platform, and cloud providers themselves. Cloud computing is emerging as one of the major enablers for the manufacturing industry; it can transform the traditional manufacturing business model, help it to align product innovation with business strategy, and create intelligent factory networks that encourage effective collaboration. Two types of cloud computing adoptions in the manufacturing sector have been suggested, manufacturing with direct adoption of cloud computing technologies and cloud manufacturing—the manufacturing version of cloud computing. Cloud computing has been in some of key areas of manufacturing such as IT, pay-as-you-go business models, production scaling up and down per demand, and flexibility in deploying and customizing solutions. In cloud manufacturing, distributed resources are encapsulated into cloud services and managed in a centralized way. Clients can use cloud services according to their requirements. Cloud users can request services ranging from product design, manufacturing, testing, management, and all other stages of a product life cycle.
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  18. T. Grubic et al., "Integrating process and ontology to support supply chain modelling," Journal of Computer Integrated Manufacturing, vol. 24, no. 9, pp. 847–863, 2011. Link
    This paper introduces an ontology model developed to support supply chain process modelling. Supply chain provides the business context for achieving interoperability of enterprise systems. It is observed that the emphasis on ontology development for enterprise interoperability could result in information models that are not relevant to real business needs. This work explicitly defines the generic business processes relevant to supply chain operations and develops the ontology that was tested in the creation of the information model to support the information exchange needs three industry case studies. It demonstrated that prior identification of processes the ontology is supposed to support facilitates its development and also its subsequent validation. This paper introduces the overall ontology development approach together with some of the findings that summarizes our experiences in developing the ontology model to support supply chain process modelling.
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  19. F. Tao et al., "Cloud manufacturing: a computing and service-oriented manufacturing model," Proc. Institution of Mechanical Engineers, Part B: Engineering Manufacture, 2011, vol. 225, no. 10, pp. 1969–1976. Link
    Combining the emerged advanced technologies (such as cloud computing, ‘internet of thing’, virtualization, and service-oriented technologies, advanced computing technologies) with existing advanced manufacturing models and enterprise ‘informationization’ technologies, a new computing- and service-oriented manufacturing model, called cloud manufacturing (CMfg), is proposed. The concept, architecture, core enabling technologies, and typical characteristics of CMfg are discussed and investigated, as well as the differences and relationship between cloud computing and CMfg. Four typical CMfg service platforms, i.e. public, private, community, and hybrid CMfg service platforms, are introduced. The key advantages and challenges for implementing CMfg are analysed, as well as the key technologies and main research findings.
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  20. B. Li et al., "Cloud manufacturing: a new service-oriented networked manufacturing model," Journal of Computer Integrated Manufacturing Systems, vol. 16, pp. 1–7, 2010. Link
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  21. H. Lin and J. Harding, "A manufacturing system engineering ontology model on the semantic web for inter-enterprise collaboration," Computers in Industry, vol. 58, pp. 428–437, 2007. Link
    This paper investigates ontology-based approaches for representing information semantics and in particular the World Wide Web. A general manufacturing system engineering (MSE) knowledge representation scheme, called an MSE ontology model, to facilitate communication and information exchange in inter-enterprise, multi-disciplinary engineering design teams has been developed and encoded in the standard semantic web language. The proposed approach focuses on how to support information autonomy that allows the individual team members to keep their own preferred languages or information models rather than requiring them all to adopt standardized terminology. The MSE ontology model provides efficient access by common mediated meta-models across all engineering design teams through semantic matching. This paper also shows how the primitives of Web Ontology Language (OWL) can be used for expressing simple mappings between the mediated MSE ontology model and individual ontologies.
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  22. M. Pratt, "Introduction to ISO 10303–the STEP standard for product data exchange," Journal of Computing and Information Science in Engineering, vol. 1, no. 1, pp. 102–103, 2001. Link
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No references have been entered yet.


  1. C2NET (2015-2018): Cloud Collaborative Manufacturing Networks. Horizon2020 Link
    A Horizon2020 project “Cloud Collaborative Manufacturing Networks C2NET” Double-click to edit the value has the purpose to create cloud-enabled tools for supporting the supply network optimisation of manufacturing and logistic assets based on collaborative demand, production and delivery plans. C2NET will have a cloud platform for cloud access to information collected from heterogeneous and distributed sources, a data collection framework for real time information collection from the different value chain enterprise systems and/or physical devices, an optimiser for advanced optimisation algorithms for the collaborative computation of production, replenishment and delivery plans, and collaboration tools to support the collaborative value chain by facilitating its diagnosis. Taking notice of these issues, CREMA framework will provide an abstraction layer for manufacturing services and manufacturing resources such as sensors, means to optimise processes both at design and run time, a semantic description of resources and processes to facilitate their usage across the supply chain, and means for making use of Big Data, knowledge, monitoring and alerting features. The C2NET Cloud Platform will provide cloud access to information collected from heterogeneous and distributed sources to create global and local production plans to optimize the processes using the data received from customers, suppliers and manufacturers. This will result on faster and more efficient decision making which have to be made due to market changes, high competition and customization requirements. C2NET Cloud Platform will allow collaborative production as production, distribution, supply and customers plans will be calculated based on real time information coming from real-world resources and considering all the actors involved in the process. As information will be available in the cloud, C2NET Cloud Platform will enable the possibility to provide relevant information to different personnel using mobile devices when and where is needed, to increase the capability for better and faster decision making when changes in production need to be done. The C2NET Data Collection Framework will enable real time information collection from the different value chain enterprise systems and/or physical devices, thus providing the C2NET platform with a holistic view on the network. This will stimulate a fast knowledge feedback loop, which will enable companies to become more effective and react faster to market changes. The C2NET Optimizer will provide advanced optimization algorithms for the collaborative computation of production, replenishment and delivery plans with the aim of optimize the use of manufacturing and logistics assets of the supply network from a holistic point of view. The C2NET Collaboration Tools will propose a concrete solution to support the collaborative value chain by facilitating the diagnosis of any source of divergence of the collaboration with regard to expected situation. Moreover the C2NET Collaboration Tools will be able to support the adaptation of the stakeholders' behaviours by implementing reaction mechanisms based on global and local optimization algorithms.
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  2. UnCoVerCPS (2015-2018): Unifying Control and Verification of Cyber-Physical Systems. Horizon2020 Link
    “Unifying Control and Verification of Cyber-Physical Systems UnCoVerCPS” is a Horizon 2020 project which has an objective to synthesise and verify controllers of the CPS on-the-fly during system execution with control unification and verification approaches.
    The project UnCoVerCPS (and IMMORTAL) may cover only lowest level of the services that will be provided by CREMA. Cyber-physical systems are very hard to control and verify because of the mix of discrete dynamics (originating from computing elements) and continuous dynamics (originating from physical elements). We present completely new methods for deverticalisation of the development processes by a generic and holistic approach towards reliable cyber-physical systems development with formal guarantees.

    In order to guarantee that specifications are met in unknown environments and in unanticipated situations, we synthesise and verify controllers on-the-fly during system execution. This requires to unify control and verification approaches, which were previously considered separately by developers. For instance, each action of an automated car (e.g. lane change) is verified before execution, guaranteeing safety of the passengers.

    In CREMA we will develop completely new methods, which are integrated in tools for modelling, control design, verification, and code generation that will leverage the development towards reliable and at the same time open cyber-physical systems. Our approach leverages future certification needs of open and critical cyber-physical systems.
  3. IMMORTAL (2015-2018): Integrated Modelling, Fault Management, Verification and Reliable Design Environment for Cyber-Physical System. Horizon2020 Link
    The project “Integrated Modelling, Fault Management, Verification and Reliable Design Environment for Cyber-Physical Systems IMMORTAL” introduces a reliable design and automated system debug into CPS modeling. To reach this aim, the project will develop a cross-layer CPS model spanning device (analogue and digital), circuit, network architecture, firmware and software layers.

    Recently, the world has seen emerging CPS modeling frameworks addressing various design aspects such as control, security, verification and validation. However, there have been no considerations for reliability and automated debug aspects of verification. The main aim is to fill this gap by introducing reliable design and automated system debug into CPS modeling. To reach this aim, the project will develop a cross-layer CPS model spanning device (analogue and digital), circuit, network architecture, firmware and software layers. In addition, a holistic fault model for fundamentally different error sources in CPSs (design bugs, wear-out and environmental effects) in a uniform manner will be proposed. Moreover, IMMORTAL plans to develop fault management infrastructure on top of the reliable design framework that would allow ultrafast fault detection, isolation and recovery in the emerging many-core based CPS networked architectures that are expected to be increasingly adopted in the coming years.

    As a result, the project will enable development of dependable CPSs with improved reliability and extended effective lifetime, aging and process variations. In line with the expected impacts of the Call, the project will have a significant impact in development time as well as maintenance costs of dependable cyber-physical systems.

    The tool framework to be developed will be evaluated on a clearly specified real-world use-case of a satellite on-board computer. However, since the results are more general and applicable to many application domains, including avionics, automotive and telecommunication, demonstration of the framework tools will be applied to CPS examples from other domains as well.
    See comment for project UnCoVerCPS
  4. EuroCPS (2015-2018): Pan-European Project to Help Innovative Companies Design and Build New Cyber-physical Systems Products for IoT Markets Link
    “European Framework on Competencies for Enabling SME from any sector building Innovative CPS products to sustain demand for European manufacturing EuroCPS” will create synergies between emerging and established organizations operating in the CPS sector. However, the project is aimed at linking the existing software in the area, provided by the partners of the EuroCPS consortium. The experience in this project can be used regarding 30 use-cases that the project claims to be demonstrated. CREMA will provide an open approach, improving the different areas in order to be able to facilitate the adoption and exploitation of the solution by the different stakeholders. The motivation of the project EuroCPS is to enable companies making new CPS products to get access to leading edge technology platforms from large companies and support from competency partners. The outcome is to boost and sustain the demand for local manufacturing and catch the IoT market by improving the European competitiveness. The first goal is to bring innovative CPS to business from any sectors within the help of networking partners. The second goal is to link user and supplier across value-chains and region within the help of the competence partners (coaching, development plan definition, service providers). Support, management and monitoring are provided by the cascade funding partners coming from RTOs and technology transfer-oriented university institutes.
    Like in this project, the integration with existing manufacturing systems will also take place in CREMA, however, the process of virtualisation of manufacturing resources and servitization of manufacturing capabilities will extend the users business models with semantics and Big Data and provide components and technology extensions for Cloud capabilities.
  5. CREMA Project (1/2015–12/2017): Cloud-based rapid elastic manufacturing. H2020 Programme of the European Commission Link
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  6. CloudSME (2013–): Simulation for manufacturing and engineering. FP7 Link
    CloudSME project will establish a Cloud Simulation Platform, which enables particularly small and medium-sized manufacturing and engineering companies (SMEs), to use state of the art simulation technology as a Service (SaaS, one-stop-shop, pay-per-use) in the cloud. Thus, they don’t have to make high investments in software licenses, required hardware or maintenance and can solely use the benefits of simulation to enhance their productivity. The FP7 project “Cloud based Simulation platform for Manufacturing and Engineering CloudSME” Double-click to edit the value has the main goal to bring the simulation and manufacturing engineering closer to cloud computing and establish Platform-as-a-Service solution. The main task here is to create a simulation platform to provide an access to multiple heterogeneous cloud resources with a high level of abstraction to users.
    CREMA accounts for more cross-organisation collaboration adapting the principles of Cloud Manufacturing: leasing and releasing manufacturing assets in an on-demand, utility-like fashion, rapid elasticity of these assets, and pay-per-use through metered service. CREMA provides manufacturing Data-as-a-Service and elastic manufacturing process execution based on that data. CloudSME methodology can be taken into account within CREMA like one of the services that could be provided: cloud-based SaaS simulation solutions for the end-users.
  7. CloudFlow (2013-2016): Cloud Computing for Engineering Workflows. FP7 Link
    “Cloud Computing for Engineering Workflows CloudFlow”: Traditionally, the European manufacturing industry is characterized by innovative technology, quality processes and robust products which have leveraged Europe's industrialization. However, globalization has exposed Europe's industry to new emerging and industrialized manufacturing markets and the current economic challenges have decelerated the internal boost and investment, respectively. Hence, new ICT infrastructures across Europe need to be established to re-enforce global competitiveness. The motivating idea behind CloudFlow is to open up the power of Cloud Computing for engineering WorkFlows (CloudFlow). The aim of CloudFlow is to enable engineers to access services on the Cloud spanning domains such as CAD, CAM, CAE (CFD), Systems and PLM, and combining them to integrated workflows leveraging HPC resources. Workflows are of key importance in today's product/production development processes were products show ever increasing complexity integrating geometry, mechanics, electronics and software aspects. Such complex products require multi-domain simulation, simulation-in-the-loop and synchronized workflows based on interoperability of data, services and workflows. CloudFlow is an SME-driven IP incorporating seven SMEs: Missler (CAD/CAM), JOTNE (PLM), Numeca (CAE/CFD), ITI (Systems), Arctur (HPC), Stellba Hydro (hydraulic machines/hydro turbines) and CARSA (business models and security). Four renowned research institutions complement the consortium: DFKI, SINTEF, University of Nottingham and Fraunhofer. CloudFlow will build on existing standards and components to facilitate an as-vendor-independent-as-possible Cloud engineering workflows platform. Open Cloud Computing Interface (OCCI), STEP (for CAD and CAE data) and WSDL (for service description and orchestration) are amongst the core standards that will be leveraged. The key aspects (from a technical and a business perspective) are: Data, Services, Workflows, Users and Business models including Security aspects. CloudFlow will conduct two Open Calls for external experiments investigating the use of the CloudFlow infrastructure in new and innovative ways, outreaching into the engineering and manufacturing community and engaging external partners. Each of these two Open Calls will look for seven additional experiments to gather experience with engineering Cloud uses and gaining insights from these experiments. CloudFlow is striving for the following impacts: a) increasing industrial competitiveness by contributing to improve performance (front-loading, early error detection, time-to-market, ...) and innovation (co-use of models, early virtual testing) and b) improving in innovation capabilities by enabling more engineers to gain insights and to create innovation by accessing 'new' tools and easing the use of Cloud Infrastructures. All in all, CloudFlow wants to contribute to a wider adaption of Cloud infrastructures and making them a practical option for manufacturing companies.
    This project of the FP7 program has its aim to provide on-demand access to scalable computational services. The project is focused on the exchange and integration of the data, adaptation and interoperability of the services, and integration and cooperation within the workflows. The CREMA project also places the emphasis on all these issues but in a way that provides full manufacturing process lifecycle: from virtualisation of the manufacturing assets to the modelling, implementation, execution, monitoring, and optimisation of the real-life manufacturing processes, providing a platform to facilitate the collaboration among the manufacturers.
  8. ManuCloud (2010-2013): The next-generation Manufacturing as a service (MaaS) environment. FP7 Link
    The transition from mass production to personalized, customer-oriented and eco-efficient manufacturing is considered to be a promising approach to improve and secure the competitiveness of the European manufacturing industries in the future, which constitute an important pillar of the European prosperity. One precondition for this transition is the availability of agile IT systems supporting this level of flexibility on the production network layer on the one hand and on the factory and process levels on the other hand. The FP7 project “The next-generation Manufacturing as a service (MaaS) environment ManuCloud” Double-click to edit the value is aimed at providing the transition from mass production to personalized, customer-oriented and eco-efficient manufacturing. To achieve this goal, the project has a major task of developing and evaluation of a suitable IT infrastructure to provide better support for on-demand manufacturing scenarios, to implement the vision of a cloud-like architecture concept, and to utilize the manufacturing capabilities of configurable, virtualised production networks, based on cloud-enabled, federated factories, supported by a set of Software-as-a-Service applications.
    Comparing to CREMA, Manucloud does not provide means to process optimisation of the manufacturing assets. In CREMA the following an approach of Autonimic Computing is applied: monitoring, analysis, planning, and execution of the processes based on a knowledge base. CREMA will automate the information transfer via monitoring with KPI-based alerting functionalities and will provide virtual models in the Cloud representing the as-is physical configuration of manufacturing and logistic assets.
This page was last changed on 9 June 2017, at 16:57.

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