Adaptive Slicing of Implicit Porous Structure with Topology Guarantee

Abstract

Adaptive slicing is an important step in 3D printing, where the thicknesses of slices are adaptively adjusted to achieve a trade-off between printing time reduction and surface quality improvement. However, in conventional slicing algorithms, the topological information inside the model is usually not considered, and then, they are not suitable for slicing porous structures with complicated inner topological structures. In this study, based on the persistent homology theory, a novel method is developed to adaptively slice implicit porous structures, which guarantees the topological correctness of the generated adaptive slice model. Given an implicit porous structure, we generate the finest slice model, and calculate the topological information of the finest slices, i.e., persistent diagram (PD) and Betti number. Next, the finest slice model is partitioned into parts based on the Betti number of the finest slices. In each part, the Betti numbers of the finest slices are the same. Moreover, in each part, we first determine the slices for printing by solving the ℓ0−norm optimization problem to optimize the printing time, and then, the thicknesses of the slices for printing are calculated by optimizing the surface quality. In this way, not only can the printing time and surface quality be optimized, but also the topological correctness of the adaptive slice model can be guaranteed. Experimental results show the effectiveness of the developed algorithm.