Comment: By developing a novel, integrated approach to providing customisable product-services based on product-usage information feedback, FALCON will contribute to lengthening the lifecycles of physical product-service components by being able to offer customers new and customised services around the core product throughout its use.
Comment: FALCON will contribute to the transition towards new generations of products allowing a stage of contemporary production of new and old products. In this area, the provision of customisable product-services will allow "old" products to be extended with "new" services, enabling a contemporary production as described above. On the basis of product-usage information feedback, "old" products can be evolved throughout their lifecycles to incorporate changing customer requirements without the need for ramping up new product production facilities.
Comment: FALCON will support the longevity of physical product-service components by providing methods and tools to enable new product services for "old" core physical products throughout their lifecycles.
Optimize production processes and producibility using Cloud/HPC-based modelling and simulation, leveraging online factury data and advanced data analytics, thus contributing to the competitiveness and resource efficiency of manufacturing SMEs.
Manufacturing SMEs are empowered to compute and solve problems that cannot be tackled without Cloud and HPC technology, making them more competitive by reducing development times for innovative products with better performance.
Comment: Real-time co-simulation methods will enable key stakeholders to simulate and forecast complex activities. Simulation will target increasing quality and flexibility as well as more efficient management of logistics by also decreasing service time and costs.
Comment: Using the COMPOSITION platform, companies will be able to enter a supply chain and broaden their clientele, thus improving their economic performance. The COMPOSITION Marketplace will facilitate matching of demand and supply across the supply chain. Buyers and sellers will have close to perfect knowledge of offers, with searchable, low latency access to information. Agent technology and automated matching of requests offers lowers transaction costs. By providing a common interface for entering the marketplace, the platform also lowers the barriers of entry. In economics terms, these are all prerequisites for competitive market (from which everyone benefits).
Comment: The project will focus on the reduction of energy consumption via the detection of excessive consumption and anomalous equipment behavior. The health of equipment will be improved, using machine learning/deep learning algorithms for predicting failures & arranging preventative maintenance. It will be possible to monetise the results into suggestions for actions at specific processes. The possibility of letting the Marketplace matchmaker take externalities into account when rating offers and partners by providing it with information on e.g. environmental performance of marketplace actors, is being examined.
Comment: Improvements of the manufacturing process by using Process Modelling and Monitoring Framework combined with Integrated Digital Factories Models will lead to better resource management and will reduce the use of resources. Optimising the process involving factories with recycling companies will generate economic savings. Monitoring the health of machinery and using predictive maintenance and reducing energy consumption will reduce not only manufacturing efficiency (less downtime & material waste) but also the environmental impact of manufacturing.
Comment: The reduction of scraps will be addressed in the sense of attempting to reduce unused material, rather than reducing the number of defective workpieces or rework operations.
Furthermore, the project will reduce the amount of scraps by optimising the processes in a way that less material has to be scrapped because certain maximal idle times are not reached.
The COMPOSITION Integrated Information Management System will be a digital automation framework that optimises the manufacturing processes by exploiting existing data, knowledge and tools, integrating them with newly installed cyber physical systems (CPS) and automation software, in order to increase productivity and to allow for dynamic adaptation to changing market requirements. CPS using IoT devices such as wireless sensors nodes (WSN) can be easily retrofitted to existing equipment and infrastructure to gather sensory data and detect anomalies as well as opportunities to improve productivity and cvcle time.
Cobots and in general HRC solutions, which can be used for multiple functions due to their flexibility in deployment, offer higher returns on investment and faster payback, compared to legacy industrial robots. Moreover, cobots’ safety features and ease of use make integration and implementation less costly than the traditional industrial robots. In high-cost countries, having skilled human workers engaged in suitable configured manufacturing, assembly, finishing and inspection operations side by side with cobots is one of the most cost- effective ways to leverage the unique value-adding capabilities of a skilled human workforce
ROSSINI will develop a Design Level that will allow users to follow and evaluate process designs on multiple dimensions, where job quality and related metrics will be the primary outcomes together with productivity, product quality and cost
HRC solutions make it possible to integrate production machinery, warehousing systems and production facilities into single human-centred cyber-physical systems. As such, the traditional frontier between the production and logistical tasks of manufacturing can be expected to become increasingly interconnected. Lastly, an increased uptake of HRC will lead to a change in the robotics value chain itself. Suppliers, integrators and users are bound to collaborate more intensively which is already leading to new business models such as rental/leasing agreements, pay-on-production, predictive maintenance, etc.
ROSSINI will deliver high performance HRC workcells, combining the safety of traditional cobots with the working speed and payloads of industrial robots, capable of optimising task execution. This will trigger manufacturers’ investment in HRC technology, increasing European factories productivity and thus competitiveness versus low-cost manufacturers.
ROSSINI ambition is to develop a framework for Human-Robot Mutual Understanding in collaborative operations which will incorporate a human-centred process design level to address and account for human factors like job quality, user experience, trust, feeling of safety, and liability, in the early design stages.
The Rossini Smart and Safe Sensing System (RS4) will combine information from several different customised sensing technologies (Vision, Laser Scanning, Radar, Mat, etc.) in order track not only the position but also the speed of each operator and object in the scene, thus ensuring operators’ safety. Moreover, the use of cobots will reduce the amount of working hours spent in physical working thus shifting the workforce away from more physically laborious tasks, towards those of assembly, programming etc. In this way, jobs could become more interesting given their need for higher levels of creativity, problem solving and decision making, definitively resulting in an improved job satisfaction.
In particular, the project aims the development of intelligent and predictive maintenance systems for the new manufacturing trends of mass customisation and individualisation. Increased reliability of production systems is considered to be crucial for securing competitive advantage for manufacturing companies. At present, maintenance in general and predictive maintenance strategies in particular are facing significant challenges in dealing with the evolution of the equipment, instrumentation and manufacturing processes they support. So, preventive maintenance strategies designed for traditional highly repetitive and stable mass production processes based on predefined components and machine behaviour models are no longer valid and more adaptive and responsive (predictive-prescriptive) maintenance strategies are needed.
Z-BRE4K will provide a modular solution for predictive maintenance that is highly customizable. Therefore, the different modules of the solution cane be sold as stand alone products or can be combined depending on the users needs. Z-BRE4K solution can be applied to both new machines and old machines that were not designed to be equipped with predictive maintenance solutions.
Z-BRE4K heavily contributes to the economic sustainability of the manufacturing sector by deploying an advanced maintenance solution aimed to attain zero unexpected breakdowns. In this regard, Z-BRE4K will avoid fatal failures, thus minimizing the breakdown times and need for spare parts and overhauls and will estimate the remaining useful life of critical subsystems of machinery, lines and shopfloor so that maintenance operations can be scheduled and optimized.
Once the Z-BRE4K system has evaluated an anomaly or a deterioration trend, the maintenance scheduling is optimized. Moreover, manufacturing machinery execution parameters can be adapted so that the remaining useful life of the incumbent system can be improved, providing Operations Managers with a flexible shopfloor.
By reducing failures, downtimes, unplanned outages the manufacturing will minimize the lead time and maximise the response time: stock-outs, lot processing delays, capacity bottlenecks will be avoided.
Z-BRE4K solution will guarantee the improved product quality since the machines will be stopped and the maintenance will be carried out before any failure occurs and defective products are manufactured.
The minimization of downtimes and failures, the optimization of maintenance operations and the reduction of row material waste will lead to an increase the in-service efficiency by 26% and increased productivity.
Z-BRE4K will provide end-users with a solution with direct benefits for the manufacturing sector in Europe such as increasing the in-service efficiency by 24% (estimated) through a combination of preventive, predictive and prescriptive maintenance strategy. Thus, companies will be able to shift some of the operative resources from maintenance to production. The benefits of Z-BRE4K strategies deployment will result in the creation of 400 jobs and over 42M€ ROI within the consortium over after the 4th year of commercialization.
Z-BRE4K will lead to the optimisation of the performance, avoiding waste due to malfunctioning machinery and increased energy consumption due to the presence of failures. the reduction of the electric costs is extimated by 10%.
Z-Bre4k will contribute to the optimisation of the manufacturing processesresulting in significantly less waste and scrap. Z-Bre4k will contribute to the reduction of defective production thanks to the optimisation of manufacturing through model-based control and improved accuracy. Moreover, it will allow to avoid overproduction that is to say manufacturing items for which there are no orders thanks to the collection of data that will control the production process producing only what is required and not overproduce.
MANUWORK Augmentede Reality based information distribution system will support the operators/workers with delivering work instructions at the working station. This will help operators coping with high product variance and customization
MANUWORK aims to design the workplace of the future where the tasks are allocated to the production line operators based on their skills while also increasing their capabilities through Human-Robot Collaboration and Augmented Reality instructions
X-act delviers advanced robotic systems involving humans and dual arm robots in cooperation, thus advancing the way manufacturing systems perform. Therefore it aims to bring about Products, processes and production systems co-evolution.