The overall aim of MOBISTYLE is to raise consumer awareness and awareness of ownership, thus empowering consumers and providing confidence of choosing the right thing, by providing attractive tailor-made combined knowledge services on energy use, indoor environment, health and lifestyle, by ICT-based solutions. This awareness will support and motivate end-users to well informed pro-active behavior towards energy use, energy efficiency and health. The objectives are: 1. To make energy use and energy efficiency understandable and easy to handle in an attractive way by unlocking and translating large data sets using data science from energy monitoring for consumers. a. To transform ‘big data’ into ‘smart data’, i.e. giving meanings to data, making data understandable and findable. b. To develop easy to use, desirable ICT-based tools which will make energy monitoring a well-accepted and attractive ‘daily activity’ for end-users as well as for professionals (building managers). 2. To provide understandable information to consumers on health and life style in relation to energy use by combining energy monitoring with monitoring indoor environmental and behavior parameters a. To combine the several low-cost, non-intrusive devices and monitoring with the energy monitoring. b. To offer the end user transparency on energy use/efficiency, indoor environment, health and lifestyle. 3. To motivate behavioral change of consumers/energy end-users by combined modular information on energy use, health and lifestyle: To transform this information into knowledge for raising awareness on energy use and behavior, thus motivating and supporting to come to a behavioral consciousness and change of lifestyle concerning energy and health. 4. To foster new business models and applications 5. To deploy and validate the developed solutions and services in different building types and user types, demonstrating a significant reduction of final energy use, prompted by these solutions.

UPTIME will seek to reframe predictive maintenance strategy by proposing a unified framework and to create an associated unified information system in alignment to the aforementioned framework. Therefore, UPTIME will extend and unify new digital, e-maintenance services and tools in order to exploit the full potential of a predictive maintenance strategy with the UPTIME solution, will deploy and validate the UPTIME solution in the manufacturing companies participating in the UPTIME consortium and will diffuse the UPTIME solution in the manufacturing community. UPTIME will enable manufacturing companies having installed sensors to fully exploit the availability of huge amounts of data with respect to the implementation of a predictive maintenance strategy. Moreover, production, quality and logistics operations driven by predictive maintenance will benefit from UPTIME. UPTIME will enable manufacturing companies to reach Gartner’s level 4 of data analytics maturity (“optimized decision-making”) in order to improve physically-based models and to synchronise maintenance with quality management, production planning and logistics options. In this way, it will optimize in-service efficiency through reduced failure rates and downtime due to repair, unplanned plant/production system outages and extension of component life. Moreover, it will contribute to increased accident mitigation capability since it will be able to avoid crucial breakdown with significant consequences. Consequently, UPTIME will exploit the full potential of predictive maintenance management and its interactions with other industrial operations by investigating a unified methodology and by implementing a unified information system addressing the predictive maintenance strategy.

Despite the proclaimed benefits (i.e. scalability, reliability, cost-effectiveness) of Future Internet (FI) technologies (i.e. edge & cloud computing, IoT/CPS) for factory automation, their adoption from manufacturers remains low for various reasons, including technology issues (e.g., poor situation awareness, limited deployments, no standards-based reference implementations) and the lack of a smooth migration path from legacy systems. FAR-EDGE is a joint effort of leading experts in manufacturing, industrial automation and FI technologies towards the smooth and wider adoption of virtualized factory automation solutions based on FI technologies. It will research a novel factory automation platform based on edge computing architectures and IoT/CPS technologies. FAR-EDGE will provide a reference implementation of emerging standards-based solutions for industrial automation (RAMI 4.0, Industrial Internet Consortium reference architecture), along with simulation services for validating automation architectures and production scheduling scenarios. FAR-EDGE will lower the barriers for manufacturers to move towards Industrie 4.0, as a means of facilitating mass-customization and reshoring. Emphasis will be paid in the study of migration options from legacy centralized architectures, to emerging FAR-EDGE based ones. FAR-EDGE will be validated in real-life plants (VOLVO, WHIRLPOOL) in the scope of user-driven scenarios (business-cases) for mass-customization and reshoring, where tangible improvements relating to reliability, productivity increase, quality cost, reduction in adaptation effort/costs will be measured and evaluated. Also, a wide range of migration scenarios will be evaluated in the scope of a CPS manufacturing testbed. FAR-EDGE will also establish a unique ecosystem for FI factory automation solutions, which will bring together the FoF and FI communities (e.g., EFFRA, Industrie 4.0, AIOTI, ARTEMIS JU) and will ensure sustainability of FAR-EDGE results.

The ROSSINI project aims to develop a disruptive, inherently safe hardware-software platform for the design and deployment of human-robot collaboration (HRC) applications in manufacturing. By combining innovative sensing, actuation and control technologies (developed by world market leaders in their field), and integrating them in an open development environment, the ROSSINI platform will deliver a set of tools which will enable the spread of HRC applications where robots and human operators will become members of the same team, increasing job quality, production flexibility and productivity. Thanks to enhanced robot sensing capabilities, the deployment of artificial intelligence to optimise productivity and safety, and natively collaborative manipulation technologies, ROSSINI will deliver high performance HRC workcells, combining the safety of traditional cobots with the working speed and payloads of industrial robots. The ROSSINI research lines will this be: SENSING: A high performance Smart and Safe Sensor System for human and robot detection & tracking, capable of quickening sensors response time by 70% CONTROL: A safety aware control architecture for robot dynamic reconfiguration, capable of reducing robot task execution time by 45% ACTUATION: An innovative, collaborative by birth robot manipulator, to be marketed through the ROSSINI platform, capable of increasing the working speed when collaborating with humans by 45% HUMAN FACTORS: Human factors analysis for mutual predictability of robot and human intentions, capable of increasing job quality index by 15% RISK ASSESSMENT: A risk assessment procedure based on new interpretation of collision values, capable of reducing (combined with other results) by 30% the time and cost of reconfiguration, and increasing the allowed robot working speed by 20% The research lines will be combined into 3 demonstrators (white goods, electronic equipment, and food packaging).

The growing complexity of modern engineering systems and manufacturing processes is an obstacle to concept and implement Intelligent Manufacturing Systems (IMS) and keep these systems operating at high levels of reliability. Additionally, the number of sensors and the amount of data gathered on the factory floor constantly increases. This opens the vision of truly connected production processes where all machinery data are accessible allowing easier maintenance of them in case of unexpected events. SERENA project will build upon these needs for saving time and money, minimizing the costly production downtimes. The proposed solutions are covering the requirements for versatility, transferability, remote monitoring and control by a) a plug-and-play cloud based communication platform for managing the data and data processing remotely, b) advanced IoT system and smart devices for data collection and monitoring of machinery conditions, c) artificial intelligence methods for predictive maintenance (data analytics, machine learning) and planning of maintenance and production activities, d) AR based technologies for supporting the human operator for maintenance activities and monitoring of the production machinery status. SERENA represents a powerful platform to aid manufacturers in easing their maintenance burdens and for this purpose will be applied in different applications. More specifically, SERENA project will focus on advancing the TRL of the existing developments into levels TRL5 to TRL7. For this purpose, SERENA consortium will fully demonstrate the proposed approach in different industrial areas (white goods, metrological engineering and elevators production) and investigate applicability in steel parts production industry (extended-demonstration activities) checking the link to other industries (automotive, aerospace etc.) showing the versatile character of the project.