COROMA project proposes to develop a cognitively enhanced robot that can execute multiple tasks for the manufacturing of metal and composite parts. COROMA will therefore provide the flexibility that European metalworking and advanced material manufacturing companies require to compete in the rapidly evolving global market. The main output of COROMA project will be a modular robotic system that will perform multitude of different manufacturing tasks in an autonomous way to adapt to the production requirements. The robot will be capable of performing drilling, trimming, deburring, polishing, sanding, non-destructive inspection and adaptive fixturing operations. Using a simple interface the robot will receive basic commands that require a minimum programming effort from the human operator. The robot will autonomously navigate in the workshop and will automatically perceive the manufacturing scene and locate the part that must be manufactured and even handle some of the required tools. Learning from previous experiences during displacement, tool grasping, part localisation and the manufacturing process itself, the robot will improve its performance. It will be able to interact with other machines in the shop floor and to work on a part even while other manufacturing operations are being performed by these other machines. Safe human-robot and machine-robot collaborations will be paramount and the robot will automatically react to the presence of both humans and other machines. The modularity of the COROMA robot will permit to customize it to meet specific requirements from different manufacturing companies. These challenges require a project consortium where the latest robotic technologies meet knowledge from manufacturing experts, including both industry and academia. COROMA project consortium presents a perfect balance between manufacturing and robotics sectors' players.

The COROUSSO project deals with the modelling and control of robots for machining operations of large composite parts and friction stir welding (FSW). As a matter of fact, those machining and welding operations are usually realized by means of expensive custom-made machines. As a consequence, manufacturers (mainly aeronautical manufacturers) require new devices to allow them to decrease the manufacturing cost as well as to improve the machining quality. It appears that the robotization of the manufacturing processes is a relevant solution. However, industrial robots are usually not stiff enough for machining operations and FSW and the quality of the resulting machined or welded parts does not respect the specifications. Accordingly, the project aims to propose three strategies to improve robot machining quality. First, the flexibilities of the robots will be taken into account in their control loop. Then, the uncertainties will be also considered in the control loop of the robots in order to obtain a robust control. Finally, the influence of the path (part to be welded or machined) placement on the machining quality will be studied. This project will also focus on the elastodynamic performance of the robots. Indeed, the wrench exerted on the end-effector of the robot due to manufacturing or the welding process can generate some distortions of the robot architecture as well as some vibrations. In order to decrease those distortions and vibrations, the elastodynamic model of the robot will be implemented in its control loop. The parameters of the elastodynamic model will be also identified experimentally beforehand. Moreover, the distortions and vibrations of the robot should be also reduced by means of force feedback control and a force sensor that will be installed between the robot end-effector and the tool. It is apparent that the better the modelling of the robot and its environment, namely the production process, the better the performance of the robot. Therefore, the kinematic and dynamic modelling of the FSW process and the manufacturing process used to machine large composite parts will be determined in the scope of this project. The partners of the project already have good knowledge of those processes but some research work has to be done in order to implement the obtained models in the control loop of the robot. This will be a major contribution of COROUSSO project. The knowledge of COROUSSO partners fit well with the project. The IRCCyN (Nantes) is well known in the field of robotics and manufacturing and mainly for its research works on robot design, modelling and control. The LCFC (Metz) has contributed a lot the last few decades in the fields of modelling and optimization of production processes like FSW. Besides, LCFC researchers took part in some industrial projects on robot control. The two industrial partners, i.e., ``l’Institut de Soudure’’ and ``Europe Technologies’’ are well known for your research works on production process and development of new manufacturing techniques. Finally, the COROUSSO project aims to provide theoretical and experimental results on the development of robotic cells dedicated to machining of large composite parts FSW. The technology transfer in the production domain will be realized by means of experimental tests conducted by the industrial partners to highlight critical operations. The contributions of the project should also help us robotize other production process later.

The objective of the SmartEmma project is to develop innovative smart and connected machine-tool for High Speed Machining (HSM). The aim is to contribute to the digital Factory of the Future. Signals from close to the process instrumentation and from the connection to the CNC (Computer Numerical Control) of the machine-tool are collected in large process database. Research will be carried out to define Key Performance Indicators (KPI) and methods for data analysis, in order to improve HSM process efficiency, through decision support tool. The approach requires an adapted modelling and efficient management of HSM process data and knowledge. KPI will be identified by Knowledge Discovery in Database and Data Mining. Continuous process improvement will be eased by an expert system and by long term learning. It will conduct to new leverages for decision making for the operational management of a machining company. Smart machines will also result from advanced perception and new real-time adaptive control capabilities.