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AEA s.r.l.
Country: Italy
12 Projects, page 1 of 3
  • Funder: EC Project Code: 785419
    Overall Budget: 2,509,380 EURFunder Contribution: 1,995,060 EUR

    According to the Global Market Forecast, there is a strong need to ramp up the productivity in the aeronautic industry and to this aim, all manufacturers are highly investing in flexible and lean manufacturing to reduce cost and boost productivity. Robotics is a key technology enabler but its adoption in the aeronautic industry is only at an early stage due to a number of barriers related to strict product requirements. Current commercial robotized assembly cells of airframe parts usually resort to high payload robots. The LABOR project has the challenging objective to overcome some of the barriers mentioned above in the automation of many critical assembly sub-operations, as drilling, inspection, sealing and fastening, by proposing a novel lean approach. The overall technological strategy consists in the adoption of small-scale robots (with the aim of saving costs and gaining flexibility) in conjunction with smart fixtures and external axes to increase their workspace. Furthermore, still in view of cost savings and the possibility to have spare parts at disposal with a very limited investment, the robotic work cell will make use of standard process tools, such as electrical drilling tools or automated fastening tools, suitable adapted to be integrated into a robot end effector compatible with quick tool-changers. Vision system will used in order to adjust the coordinates where the robot has to drill based on real time scanning of the sub-components and to check the quality of the holes to guarantee a high standard of the process. Each components of the self adaptive robotic cell will be considered as a Cyber Physical System and a distributed intelligence approach will be adopted. The consortium has experience in the proposed approach for the aeronautic sector and has the needed competences to bring the proposed concepts in the real environment and to exploit the results of the project.

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  • Funder: EC Project Code: 246203
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  • Funder: EC Project Code: 608679
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  • Funder: EC Project Code: 723764
    Overall Budget: 5,027,620 EURFunder Contribution: 4,012,460 EUR

    Multi-stage manufacturing, which is typical in important industrial sectors such as automotive, house hold appliance and semiconductor manufacturing just to name few, is inherently complex. The main idea of GO0D MAN project is to integrate and combine process and quality control for a multi –stage manufacturing production into a distributed system architecture built on agent-based Cyber-Physical Systems (CPS) and smart inspection tools designed to support Zero-Defect Manufacturing (ZDM) strategies. Data analytics tools provide a mean for knowledge build-up, system control and ZDM management. Real time and early identification of deviations and trends, performed at local level, allow to prevent the generation of defects at single stage and their propagation to down-stream processes, enabling the global system to be predictive (early detection of process faults) and proactive (self-adaptation to different conditions). The GO0D MAN project is based on the results of previous successful EU projects and integrates them to realize and deploy a Zero Defect Manufacturing framework for multi-stage production lines, in collaboration with industry partners, a system integrator, two technology providers and three end users. The use cases are representative of key European industrial sectors and have different types of multi-stage production systems: the first use case concerns highly automated serial mass production of automotive components, the second use case is about batch production of high precision mechanical components for automotive electro valves, the third use case produces professional customized products such as ovens for restaurants. Successful completion of this project will provide a replicable system architecture for ZDM. The results will be broadly applicable in a variety of industries to improve the overall quality and productivity of production systems.

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  • Funder: EC Project Code: 256768
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