Laser Powder Bed Fusion has become a widely adopted method for metal Additive Manufacturing (AM) due to its ability to mass produce complex parts with increased local control. However, AM produced parts can be subject to undesirable porosity, negatively influencing the properties of printed components. Thus, controlling porosity is integral for creating effective parts. A precise understanding of the porosity distribution is crucial for accurately simulating potential fatigue and failure zones. Previous research on generating synthetic porous microstructures have succeeded in generating parts with high density, isotropic porosity distributions but are often inapplicable to cases with sparser, boundary-dependent pore distributions. Our work bridges this gap by providing a method that considers these constraints by deconstructing the generation problem into its constitutive parts. A framework is introduced that combines Generative Adversarial Networks with Mallat Scattering Transform-based autocorrelation methods to construct novel realizations of the individual pore geometries and surface roughness, then stochastically reconstruct them to form realizations of a porous printed part. The generated parts are compared to the existing experimental porosity distributions based on statistical and dimensional metrics, such as nearest neighbor distances, pore volumes, pore anisotropies and scattering transform based auto-correlations.
翻译:激光粉碎贝化裂变已成为一种广泛采用的金属添加剂制造方法(AM), 因为它能够通过增加本地控制来大规模生产复杂的部件。 但是, AM 生产的部件可能会受到不可取的孔隙, 从而对印刷部件的特性产生不利影响。 因此, 控制孔隙是创造有效部件所不可或缺的。 精确了解孔隙分布对于准确模拟潜在疲劳和故障区至关重要。 先前关于合成孔隙微结构的研究成功地产生了高密度、 异质孔径分布, 但往往不适用于稀释、 边界依赖孔隙分布的情况。 我们的工作弥补了这一差距,提供了一种方法,通过解构筑代问题到其构件部分来考虑这些限制因素。 引入了一个框架, 将基因反向网络与Mallat 散开的基于自动化的自动关系联系法结合, 以构建对个别孔径和表面粗糙的新认识, 然后对它们进行结构重建, 形成多孔径的碎屑部分。 生成的部件与最新的实验性空间分布相比, 以现有实验性空间分布为基础, 。