USING ROBOT AUTOMATED LASER PROFILE SENSING FOR PART DEFECT INSPECTION SUPPORTING COLD SPRAY REPAIR

抄録

Cold spray is a promising area of additive manufacturing concerning the repair of parts with complicated surface geometry in various application domains. However, as currently cold spray is mainly human-operated, it is limited in both precision and efficiency. Integration of this technique with a robot can unleash its potential with improvements to both limitations. A crucial component in a robotic system of this nature is part inspection to identify and quantify defect. We propose the use of a laser profiler incorporated into the robot arm to facilitate part surface examination throughout cold spray repair. of dents and wear spots. Focusing on a selectable area of a part surface, the laser profiler automatically captures high precision (1µm repeatability) overlapping point clouds of the surface and its neighborhood which are stitched together through ICP to return a single point cloud detailing the inspection area. This point cloud is subsequently fed through a suite of comparison algorithms along with the part’s CAD model to isolate and quantify areas of defect. An unworn area comparison to the corresponding CAD model is used to isolate the noise associated with the motion of the robot end-effector. This is achieved by adjusting the point cloud offset to the CAD model through a Möller-Trumbore ray-triangle intersection algorithm. A geometric dissimilarity approach is then applied to isolate points that correspond to surface defects. This approach inspects Euclidean distance and Gaussian curvature at each point in the captured scan with respect to the CAD model and automatically separates points with high disagreement. Implementation results on benchmark specimen demonstrate the high accuracy and robustness of the proposed system.