The R&D Cluster Towards agile manufacturing systems and processes incorporates the following research priorities: RP1.1 – Software for flexible and reconfigurable machinery and robots Highly dynamic market demands and changing customer requirements for product personalisation are driving European factories to modify their asset-instalment bases with flexible and reconfigurable machinery and robots. Software for dynamic reconfigurations would not only increase the throughput of factories but also integrate with existing backend systems for design and manufacturing with the objective of reducing changeover time/cost, tooling and programming effort. Furthermore, generic software solutions for reconfigurable machinery and robots will open up new business opportunities through the concept of factory leasing, where different manufacturers could lease an existing factory setup to manufacturing similar goods but with different configuration needs. RP1.1 would implement key ICT recommendations CL3 Collaborative service management to tackle complexity and optimise operations, CL4 Collaborative design and manufacturing for better products and IN9 Big data analysis and real-time decision making. Industrial challenges Dynamic personalisation of products and frequent changes in production and machine reconfiguration; Building software to capture these dynamic needs as well as integrate with systems and machinery; Reducing consumption of energy as well as of resources – materials, water, etc.; Assisted setup of machines and the work pieces; Automatic referencing and calibration capabilities for machinery; and Implementation of geometrical measurement and integrity inspection on the machines. Potential outcomes Reduction of time and cost of factory and machine reconfiguration; Reduction of expert programming needs and time devoted to it; Reduction of energy consumption during machine reconfiguration and subsequent use Reduction of scrap – defective work pieces – and reworking; Reduction of manufacturing steps through increased machine functionality; and Implementation of leased-factory concept. ICT research requirements Develop multidisciplinary models and tools for designing flexible and easily reconfigurable systems and machines, including dynamic simulation and monitoring of consumption of energy and other resources; Develop decentralised control systems including operational schemes and intelligent control patterns; Conceive open IT platforms for integration and networking of control systems; Develop intelligence and signal-processing solutions for self-adjustment and correction; Develop virtual metrology and real-time capabilities; and Generate a formal repository incorporating all the necessary knowledge for designing flexible and reconfigurable systems and machines. Ambition Radar TRL Impact Factor 1.8 Horizon 2020 By 2016 By 2018 By 2020
RP1.3 – Adaptive process automation and control for a sensing shopfloor Intelligent plug-and-play systems will feature sensing and actuator structures integrated with adaptive control systems supported by active compensation features for fully optimising the performance of the manufacturing systems in terms of autonomy, reliability and efficiency along their lifecycle. This will enable the development of embedded distributed control systems architectures with end-to-end device-integration capabilities as well as real-time data processing and KPI calculation capabilities. RP1.3 will implement key ICT recommendations IN9 Big data analysis and real-time decision making, IN10 Intelligent visualisation of big data, and IN11 High-performance simulation and analysis in the cloud. Industrial challenges Ensuring competitive position through efficiency of manufacturing equipment; Increasing precision performance by improving robustness against external influences; Providing the capability to measure cost-effectively all critical parameters of manufacturing processes in real time; Ensuring an increased compatibility and wide interoperability of plug–and-play systems – modular architecture, interface mechanism, programming interfaces, etc.; and Providing the capability to process real-time inputs and process models to create an executable process model integrated with the controller. Potential outcomes Improved productivity and zero ramp-up time of production processes; Adaptive, high-performance control solutions able to deliver zero-defect processes and optimised performances of manufacturing processes considering dynamic requirements and surrounding conditions; Improved and more predictable performance of manufacturing systems their lifecycle, covering reliability, maintainability, cost and energy efficiency; Improved motion accuracy and robustness in mechatronic systems; and Improved accuracy of the process – zero scrap and reworking – through comparison with models and complete adaptation of the machine and process. ICT research requirements approaches to build low-power embedded distributed control systems architectures; Real-time simulation embedded in the control involving high-performance computing; Integration of plug-and-play components with the production process; Data-processing and data-mining technologies capable of extracting the knowledge and model of machine and process parameters across the lifecycle; Software capable of monitoring KPIs and lifecycle parameters; Automatic adaptation of controllers and automatic communication to backend systems such as MES and ERP. Ambition Radar TRL Impact Factor 2.0 Horizon 2020 By 2016 By 2018 By 2020
RP1.4 – Dynamic manufacturing execution environments for smarter integration Legacy manufacturing execution systems have non-modular architecture and will not cope with the dynamic nature of future manufacturing processes. Next-generation MES would require constant optimisation of quality and resource use. Furthermore, the amount of knowledge extracted from the level of automation should be fully exploited by MES. MES would need address the dynamism of environments and facilitate sustainable manufacturing through optimisation of knowledge-based systems and integration with supply-chain processes. These should furthermore be condition based, exploit experience on the shopfloor and facilitate self organisation of production systems. RP1.4 would implement the key ICT recommendations CL3 Collaborative service management to tackle complexity and optimise operations, CL4 Collaborative design and manufacturing for better products, IN9 Big data analysis and real-time decision making and IN11 High-performance simulation and analysis in the cloud. Industrial challenges Reduction of lead towards just-in-time deliveries; High process transparency through appropriate human-machine interfaces; Reliable and friendly-to-use modelling systems for the process and production system; Real-time monitoring and diagnosis at different levels of the plant; Reduction of energy consumption through more intelligent production planning; and Improved and efficient use of complex manufacturing environments which include fixed and mobile robotised systems. Potential outcomes Integration with automation level to provide flexible and dynamic MES to evolve with highly agile and reconfigurable future manufacturing systems; Full integration of MES with enterprise information systems to use of internal and supply network resources better to demand changes; Full integration of MES and automation systems for visualising the engineering value chain and traceability of manufactured products; and What-if scenario analysis of future outcomes enabling efficient use of resources. ICT research requirements High-performance computing leveraging the to deal with the large amount of data coming from the level of automation and real-time reactivity to perform optimisation and what-if scenario analysis; Complex event processing (CEP) and data-stream analysis for generating real-time production performance indicators; optimisation of production schedules in real time based on implementation and adaptation of self-learning approaches to MES; Next-generation software architectures which aggregate conventional production metrics with sustainability metrics; and New MES-based on software architectures which are modular in nature, state-of-the-art-based and leverage the best practices of cloud deployment. Ambition Radar TRL Impact Factor 2.2 Horizon 2020 By 2016 By 2018 By 2020
RP3.1 – Enhanced visualisation of complex manufacturing and production data As data volumes on the shopfloor and at plant levels continue to increase and manufacturing systems become more integrated, maintaining situation awareness and coping with information overload pose a serious challenge. Future ICT solutions should focus on novel visualisation techniques which will abstract relevant data from real-world resources and business systems, and display relevant information to knowledge workers and decision makers. These data-visualisation systems should be role based, maintaining a level of abstraction and anonymity based on viewer access levels. RP3.1 would implement the key ICT recommendations CL5 Collaborative knowledge management for value creation, CN8 social network for HMI and IN10 Intelligent visualisation of big data. Industrial challenges Increasing volumes of data are produced during different production phases that need to be processed for better comprehension; Simulations, predictions and optimisations of factory and product lifecycles are key factors to guarantee efficiency and sustainability; and Visualisation should support the seamless integration of lifecycle steps and provide agile methods to recondition manufacturing processes and productions plans. Potential outcomes More efficient and faster decision making through accurate and filtered information; Better factory knowledge and increased competitiveness through an informed workforce; New of service related to data visualisation techniques; and Cost savings and timely detection of exception conditions and faults. ICT research requirements Reducing the complexity of high volume data through appropriate data clustering and visualisation techniques; interaction methods including means for multi-cultural interactions – data schema mapping and translation; Holistic approaches for visualisation of models and simulation results of manufacturing systems for better human understanding; 3D visualisation of data with zoom-in and zoom-out browsing capabilities and mixed reality techniques; Visualisation sharing using hosted social collaboration platforms which enable workers and decision makers to discuss and brainstorm factory problems and process improvement strategies; Leveraging new user-interface technologies such as Silverlight, and next-generation algorithms for developing engaging and immersive visualisations; and Mobile apps for workers who use data-push mechanism to display KPIs and exception conditions. Ambition Radar TRL Impact Factor 2.2 Horizon 2020 By 2016 By 2018 By 2020
RP4.5 – Complex event processing for state detection and analysis in supply networks Connected objects representing the Internet of Things in supply networks will generate copious amount of data in the form of events. These events will be distributed in nature and display non-deterministic and asynchronous characteristics. Global state detection as well as discrete/continuous query processing would therefore be a challenge in view of the distributed nature of events. ICT research in CEP should devise solutions for more responsive supply networks with capability for comprehensive monitoring and management of events, exceptions and ‘what-if’ scenarios. RP4.5 contributes to the realisation of ICT recommendations IN9 Big-data analytics and real-time decision making and IN10 Intelligent visualisation of big data. Industrial challenges Lack of integrated CEP engines in conventional business systems makes detection of states and queries intractable; Since events could be generated at any level ranging from shopfloor to the business layer across any enterprise in the network, detection and query processing would be challenging in presence of and asynchronous characteristics; and Persistence and subscription of events in scenario is a challenge. Potential outcomes Increase of business and intelligence across all tiers of an enterprise, resulting in faster reaction time to shopfloor alerts or changing logistics situations; Enable decision makers to monitor exceptions across geographically-distributed plants; and Business opportunities for SMEs to configure and sell analytical services on large enterprise data through the Manufacturing App Store. ICT research requirements Adapt existing CEP algorithms used in the financial world for detecting states and processing queries in distributed deployment of Internet of Things in supply network of Manufacturing 2.0 enterprises; Investigate formulation of predicates – query – for detection and ways to filter, aggregate and correlate results from multiple predicates; Explore complexities of detecting different classes of monotonic as well as non-monotonic predicates in conjunctive, disjunctive and relational queries; optimisation techniques for varying window and predicate size within CEP engines; and Issues related to persistency, subscription and brokerage of events generated within network of Internet-of-Things resources. Ambition Radar TRL Impact Factor 2.2 Horizon 2020 By 2016 By 2018 By 2020