Three-Dimensional Reconstruction of Defects and Structures in Additively Manufactured Parts with Automated Serial Sectioning
Metal additive manufacturing (AM) processes have been demonstrated to be effective at reducing costs and lead times associated with complex components for space flight applications. Laser powderbed fusion (L-PBF) is a commonly used AM technology due to the ability to produce complex parts with fine feature resolution in a wide variety of alloys and applications. L-PBF, like many other manufacturing processes, can produce minor flaws in parts when in nominal operation as well as process-escape defects when process abnormalities occur. The effects of the flaws and methods of detecting the flaws are a subject of interest to understand the difficulties in detecting these flaws with current technology and how much risk the flaws or defects pose to potential flight parts. Using a RoboMet.3D automated serial sectioning system, seeded defects as well as minor process flaws can be imaged and reconstructed in three dimensions to compare to non-destructive evaluation (NDE) techniques, such as x-ray computed tomography (CT), neutron CT, and in-situ monitoring. The RoboMet automates the metallography process by automatically grinding, polishing, and imaging samples in a single system and providing the control data for NDE comparisons to know the real size of defects built into coupons. These comparisons provide an understanding behind the technological limitations of the NDE techniques for different alloys. The same serial sectioning methods have also been utilized to characterize the surfaces of parts to reconstruct the surfaces and take measurements of internal features not easily examined with non-destructive methods. Using the RoboMet, fine lattice structures built with L-PBF have been characterized to determine the actual thicknesses of struts and density of the lattice structures. These structures have been used as finer build supports for the L-PBF process, designs for fine catalysts, and other design considerations for small components. The RoboMet data helps to inform the modeling and design efforts around these fine components.