High-fidelity tomographic additive manufacturing for large-volume and high-attenuation situations using expectation maximization algorithm

Abstract

Tomographic volumetric additive manufacturing (VAM) is an emerging technology that employs patterns projected from all angles surrounding the build volume to tomographically reconstruct a three-dimensional (3D) exposure dose distribution and subsequently photopolymerize the entire target structure. However, the exposure dose distribution reconstructed using the tomographic projection method inherently includes fuzzy boundaries and high background dosages. Consequently, achieving high-fidelity printing using this technique is challenging, particularly when printing large volumes or using high-attenuation materials. The computational algorithm for generating projection patterns plays a significant role in reconstructing high-quality 3D exposure dose distributions and achieving a high-fidelity print. In this study, an expectation-maximization-based method that is more suitable for situations with large volumes or high attenuation is developed. The simulation and printing results reveal that the proposed method can achieve improved reconstruction and high-fidelity printing when printing large volumes or using high-attenuation materials. The proposed method can therefore improve the reliability and robustness of 3D printing technology, particularly in challenging scenarios.