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Geometric accuracy control is critical for precision additive manufacturing (AM). To learn and predict the shape deformation from a limited number of training products, a fabrication-aware convolution learning framework has been developed in our previous work to describe the layer-bylayer fabrication process. This work extends the convolution learning framework to broader categories of 3D geometries by constructively incorporating spherical and polyhedral shapes into a unified model. It is achieved by extending 2D cookiecutter modeling approach to 3D case and by modeling spatial correlations. Methodologies demonstrated with real case studies show the promise of prescriptive modeling and control of complicated shape quality in AM. 

There is a growing importance in characterizing 3D shape quality in additive manufacturing (a.k.a. 3D printing). To accurately define the shape deviation between the designed product and actual build, shape registration of scanned point cloud data serves as a prerequisite for a reliable measurement. However, manual registration is currently heavily involved, for example, in obtaining initial matching of the design and the scanned product based on landmark features. The procedure can be inefficient, and more importantly, introduce potentially large operator-to-operator variations for complex geometries and deformation. Finding a sparse shape correspondence before refined registration would be meaningful to address this problem. In that case, automatic landmark selection has been a challenging issue, particularly for complicate geometric shapes like teeth. In this work we present an automatic landmark selection method for complicated 3D shapes. By incorporating subject matter knowledge (e.g., dental biometric information), a 3D shape will be first segmented through a new density-based clustering method. The geodesic distance is proposed as the distance metric in the revised clustering procedure. Geometrically informative features in each segment are automatically selected through the principal component analysis and Hotelling's T2 statistic. The proposed method is demonstrated in dental 3D printing application and could serve as a basis of sparse shape correspondence.