OPTIMISED aims to develop novel methods and tools for deployment of highly optimised and reactive planning systems that incorporate extensive factory modelling and simulation based on empirical data captured using smart embedded sensors and pro-active human-machine interfaces. The impact of energy management on factory planning and optimisation will be specifically assessed and demonstrated to reduce energy waste and address peak demand so that operations that require or use less energy, can allow this excess energy to be re-routed to local communities. The OPTIMISED environment will use semantically enriched process modelling, big-data generation, capture and perform analytics to effectively support planning specialists, manufacturing engineers, team leaders and shopfloor operatives throughout the systems lifecycle. These next generation manufacturing systems supported by data rich manufacturing execution systems with OPTIMISED technology will support a dramatic improvement in system performance, improved operational efficiency and equipment utilisation, real-time equipment and station performance monitoring, adaptation and resource optimisation. The OPTIMISED vision will be achieved by developing systems which are able to: 1. Monitor system performance through an integrated sensor network, automatically detecting bottlenecks, faults and performance drop-off 2. Continuously evolve to respond to disruptive events, supply chain disruptions and non-quality issues through factory simulation modelling 3. Improve understanding and monitoring of energy demand curve and energy usage per industrial process and globally improve efficiency of production line through reduced energy waste 4. Understand potential benefits, added value and impacts of participating in Demand Side Response (DSR) processes and becoming an active player in the changing energy industry, instead of remaining a conventional passive element that simply acquires a service from energy providers

Glass fibre production, precision machining of large parts (e.g. wind turbines), additive manufacturing of medical implants and high-temperature metal production are manufacturing examples with processes that are difficult to automate. The main reasons for the actual labour intensive efforts in these scenarios are lack of full understanding and control over the individual manufacturing steps and the high complexity of the tasks. This has severe impacts on sustainable growth, manufacturing productivity, efficiency and flexibility due to the large amount of unpredictive waste in production and processing time. COGNIMAN is devoted to improving these situations by developing and demonstrating a novel concept of ?digital cognitive smart manufacturing? that will shift the future design of manufacturing processes towards autonomous and predictive manufacturing with improved flexibility, safety and efficiency. This initiative will provide the means to facilitate flexible, resilient, reconfigurable, safe, sustainable, and efficient smart manufacturing by integrating key technologies. They include simulations, digital twins, advanced sensors, machine learning toolbox and cognitive robotics integrated in human-centric modular toolboxes that can be easily adapted to substitute varying manual manufacturing processes. By tackling significant challCOGNIMAN will provide the means to facilitate flexible, resilient, reconfigurable, safe, sustainable, and efficient smart manufacturing by integrating simulation, models, digital twins, sensors, Artificial Intelligence (Machine Learning), data processing and analytics, robotics, and autonomous systems in a human-centric modular toolbox that can be easily adapted to new manufacturing processes and environments with the ultimate objective of boosting the European technology and manufacturing sectors competitiveness towards industrial leadership in global markets, as well as to reduce the environmental footprint of manufacturing activities.

Smart Manufacturing is believed to play a critical role in maintaining the competitiveness of organisations, by supporting them at different levels such as process optimisation, resource efficiency, predictive maintenance and quality control. Nevertheless, AI technologies which are currently and rapidly penetrating industrial sectors at those levels remain essentially narrow AI systems. This is due to the lack of self-adaptiveness in the AIs capability to assimilate and interpret new information outside of its predefined programmed parameters. This mean that AI systems are tailored for solving specific tasks on a specific predefined setting and changes in the underlying setting usually requires system adaption ranging from fine-grained parameter adaptations to fully-fledged re-design and re-development of AI systems. TEAMING_AI project aims at a human AI teaming framework that integrates the strengths of both, the flexibility of human intelligence and scale-up capability of machine intelligence. Human AI teaming is equally motivated to meet the increased need for flexibility in the maintenance and further evolution of AI systems, driven by the increasing personalization of products and service, as well as tackling the barriers of user acceptance and ethical challenges involved in the collaborative environments where artificial intelligence will be used, in order AI can be considered as “teammate” rather than as a threat. The TEAMING.AI project will be run over 36 months with a work plan divided into 9 Work Packages. Work Packages from 1 to 5 are devoted to the development of new technology to enhance the interaction between human and machine. Furthermore, Work Packages 6 and 7 wrap the development of 3 use case scenarios. Finally, two final Work Packages (8 and 9) will work respectively on the dissemination, exploitation of results and coordination of the project in a transversally way to the above mentioned WPs.

Manufacturing industries continuously face the challenge of delivering high-quality products under high production rates while minimizing non value-adding activities. The recent COVID-19 pandemic is causing manufacturers to rethink and reassess their global supply chains and the flexibility of their production sites. Resilience means the ability to withstand difficult situations, while flexibility can be considered as the ability to accommodate changes without incurring significant extra costs. Production processes demanding high human skill such as forming processes, requires readjustment of the process parameters of all production steps as a new product evolves. The deficiencies can be attributed largely to the lack of efficient ways for trusted data sharing among the stakeholders without interoperability barriers. There is a need to be able to determine when such changes lead to deterministic-chaotic behavior with far reaching consequences. FLEX4RES provides an open platform to support production networks' reconfiguration for resilient manufacturing value chains. FLEX4RES will utilize platform-based manufacturing that builds on the state-of-the-art Gaia-X and IDS technologies for data-sharing in the horizontal supply chain and the Asset Administration Shell (AAS) that is to implement intra-factory reconfiguration practices. FLEX4RES considers the Digital Twin of the value-adding network a key enabling technology to achieve reconfiguration processes in highly flexible production systems and networks. The key element of technology linkage is represented by the Self-Descriptions with linked, standardized information models, especially in terms of resilience. The developed platform and specialized hardware aim to improve the existing industry-established lean management approaches related to Reconfiguration Management through the digitalization of the production, characterized as Industry4.0, by allowing for the information sharing between value chain stakeholders.