In-process 4D reconstruction in robotic additive manufacturing

Robotic additive manufacturing using a cold spray deposition head attached to a robotic arm can deposit material in a solid state with deposition rates in kilogrammes per hour. Under such a high deposition rate, the complicated interplay between the robot’s motion, gun standoff distance, spray angle, overlapping, and the interaction of supersonic powder particles with a growing structure could cause overabundance or deficiency of material build-up. Over time, the accumulation of these discrepancies can negatively affect the overall shape and size of the final manufactured object. In-process spatio-temporal 3D reconstruction, also known as 4D reconstruction, could allow for early detection of deviations from the design, thus providing the opportunity to rectify at an early stage, making the process more robust, efficient and productive. However, in-process model reconstruction is challenging due to the dynamic nature of the scene (e.g. sensor and object relative movements), the three-dimensional growth of a time-varying build object, the textureless nature of build surfaces, and its computational complexity. We propose a real-time, in-process 4D reconstruction framework for free-form additive manufacturing processes, such as cold spray that deals with a real-time dynamic and evolving scene built by incremental deposition of materials. In our approach, temporal point clouds from three cameras are acquired and segmented to extract the region of interest (build object). The subsequent multi-temporal and multi-camera registration of the segmented 3D data is addressed by combining geometrically constrained Fiducial marker tracking and plane-based registration without drift accumulation. Finally, the registered point clouds are fused via voxel fusion of growing parts to reconstruct the 3D model of the object with smoothened surfaces. The proposed solution is deployed and verified in a robotic cold spray cell with different test scenarios and shape complexities.