Extended Fabrication-Aware Convolution Learning Framework for Predicting 3D Shape Deformation in Additive Manufacturing

Abstract

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-by-layer 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 cookie-cutter 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.