Virtual Volumetric Additive Manufacturing (VirtualVAM)

TH Weisgraber and MP de Beer and SJ Huang and JJ Karnes and CC Cook and M Shusteff, ADVANCED MATERIALS TECHNOLOGIES, 8 (2023).

DOI: 10.1002/admt.202301054

Tomographic volumetric additive manufacturing (VAM) produces arbitrary 3D geometries by exposure of a rotating volume of photopolymer resin to tomographically-patterned illumination. This enables high speed, layer- less printing of parts from a wide range of photopolymers not amenable to layer-by-layer processes. Since the entire geometry is produced at once over the course of a few seconds to minutes, molecular diffusion length scales become significant to the printing process. Understanding these molecular reaction and diffusion processes is imperative for advancing VAM to a usable technology. These processes are experimentally very difficult to monitor and measure. Herein, VirtualVAM - a simulation framework for modeling the tomographic VAM process, is developed and experimentally validated. VirtualVAM simulates reaction, diffusion, and heat generation processes over the course of a print with single-voxel resolution. From a few experimentally-determined input parameters and a set of images for projection, VirtualVAM is able to generate a large spatio-temporal data set for any given tomographic VAM print. Using VirtualVAM, a number of experimentally-unattainable aspects of the VAM process are investigated such as single-voxel conversion profiles, effect of molecular oxygen, and stopping time determination. VirtualVAM also enables the optimization of exposure patterns to further improve contrast between in-part and out-of-part delivered dose. This study introduces VirtualVAM, a computational framework for modeling the tomographic Volumetric Additive Manufacturing (VAM) process. The ability to simulate the production of an entire part provides insight into the interaction between polymerization kinetics and mass transport, which is imperative for improving the resolution and throughput of VAM. We experimentally validate the model and highlight optimization strategies to reduce build errors.image

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