Automation of a Repair Process by Robot-Guided Cold Spray

Abstract The growing importance of conserving natural resources is driving the demand for advanced repair techniques. In this context, cold spray is emerging as a highly promising technique for repairing metallic components. This process offers significant advantages, such as the deposition of heat- and oxidation-sensitive materials. However, to utilize cold spray as an efficient and reliable repair technique, it is essential to integrate automation and robotics. This work proposes a concept for the automation of a repair process by robot-guided cold spray. The comprehensive workflow begins with the sensor-based inspection of the damaged region, followed by the automated extraction of the damage volume to define the dimensions for the subsequent material removal. In the next step, pre-machining is virtually planned and simulated to prepare the component surface for material deposition by removing the damaged volume while considering the surface preconditions for cold spray. This is followed by the planning and simulation of the material deposition for effective and material-efficient filling of the machined cavity. This includes automated planning of the robot trajectories, starting with initial trajectory planning, followed by trajectory optimization using mathematical optimization and a material deposition model to account for the various requirements and to ensure an optimal repair process. Once the simulations of pre-machining and material deposition have been completed, the control codes are transferred to the real robot control in the laboratory. Finally, the concept enables sensor-based inspection of the material deposit, allowing the deposit height to be compared with the simulation result as quality proof. The full workflow of this concept has been successfully applied by simulation and laboratory experiments. The results prove the utility of this concept and demonstrate the successful automation of a repair process by robot-guided cold spray.