Wei Xiangzhi, Qiu Siqi, Zhu Lin, Feng Ruiliang, Tian Yaobin, Xi Juntong, Zheng Youyi
IEEE Trans Vis Comput Graph. 2018 Oct;24(10):2799-2812. doi: 10.1109/TVCG.2017.2767047. Epub 2017 Oct 27.
Minimizing support structures is crucial in reducing 3D printing material and time. Partition-based methods are efficient means in realizing this objective. Although some algorithms exist for support-free fabrication of solid models, no algorithm ever considers the problem of support-free fabrication for shell models (i.e., hollowed meshes). In this paper, we present a skeleton-based algorithm for partitioning a 3D surface model into the least number of parts for 3D printing without using any support structure. To achieve support-free fabrication while minimizing the effect of the seams and cracks that are inevitably induced by the partition, which affect the aesthetics and strength of the final assembled surface, we put forward an optimization system with the minimization of the number of partitions and the total length of the cuts, under the constraints of support-free printing angle. Our approach is particularly tailored for shell models, and it can be applicable to solid models as well. We first rigorously show that the optimization problem is NP-hard and then propose a stochastic method to find an optimal solution to the objectives. We propose a polynomial-time algorithm for a special case when the skeleton graph satisfies the requirement that the number of partitioned parts and the degree of each node are bounded by a small constant. We evaluate our partition method on a number of 3D models and validate our method by 3D printing experiments.
最小化支撑结构对于减少3D打印材料和时间至关重要。基于分区的方法是实现这一目标的有效手段。尽管存在一些用于实体模型无支撑制造的算法,但从未有算法考虑过壳模型(即空心网格)的无支撑制造问题。在本文中,我们提出了一种基于骨架的算法,用于将3D表面模型划分为最少数量的部分,以便在不使用任何支撑结构的情况下进行3D打印。为了实现无支撑制造,同时最小化由分区不可避免地引起的接缝和裂缝的影响,这些接缝和裂缝会影响最终组装表面的美观和强度,我们提出了一个优化系统,在无支撑打印角度的约束下,最小化分区数量和切割总长度。我们的方法特别针对壳模型量身定制,也适用于实体模型。我们首先严格证明了该优化问题是NP难的,然后提出了一种随机方法来找到目标的最优解。当骨架图满足分区部分数量和每个节点的度数由一个小常数界定的要求时,我们针对特殊情况提出了一种多项式时间算法。我们在多个3D模型上评估了我们的分区方法,并通过3D打印实验验证了我们的方法。