Data-Driven Material Modeling with Functional Advection for 3D Printing of Materially Heterogeneous Objects

Abstract

We present a data-driven approach for the creation of high-resolution, geometrically complex, and materially heterogeneous 3D printed objects at product scale. Titled Data-driven Material Modeling (DdMM), this approach utilizes external and user-generated data sets for the evaluation of heterogeneous material distributions during slice generation, thereby enabling the production of voxel-matrices describing material distributions for bitmap-printing at the 3D printer's native voxel resolution. A bitmap-slicing framework designed to inform material property distribution in concert with slice generation is demonstrated. In contrast to existing approaches, this framework emphasizes the ability to integrate multiple geometry-based data sources to achieve high levels of control for applications in a wide variety of design scenarios. As a proof of concept, we present a case study for DdMM using functional advection, and we demonstrate how multiple data sources used by the slicing framework are implemented to control material property distributions.