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The main objective of CANNIER is to develop a robotized moulding cell, capable of automatically laminating carbon fibre reinforcements that have been pre-impregnated (prepreg) with activated resin. The aim is to make the manufacturing process more agile, overcoming the bottlenecks of manual moulding. High performance and production repeatability are accomplished, with a reduced cost of the composite parts. CANNIER will be a flexible system that does not require customization to specific geometries, adapting the production process to a wide range of parts with basic reprogramming. The mold with the prepreg ply is placed in front of the robot by an operator. The CAM software that we are developing generates the lamination trajectories based on piece CAD and mold position, that can be adjusted through the HMI. The trajectories are translated into a robot program, that is performed by the robot arm using the designed lamination tools and their changer. The operator replaces the mold with the laminated piece and the process starts again. The system dynamically adjusts the pressure it is applying through a control loop closed by a pressure sensor placed on the end effector of the robot.

Roboticssa is developing a CAM software for trajectories generation will make the process more agile, proposing the trajectories performed by the robot on the base of the parts 3D CAD models and the selected tools; the CAM tool will allow expert operators to modify the trajectories based on their experience and to select the best-suited tool for each step of the lamination with an intuitive user interface (HMI). The software will also ensure the security of the data introduced in the system, protecting confidential information against potential cyber-attacks. A simple reprogramming guarantees significant reductions of the time needed to reconfigure the system for the manufacturing of different products, making the process more agile.


This SME demonstration of TRINITY project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825196.