With additive manufacturing (AM) it became possible to create complex geometries that would be difficult or impossible to produce using traditional manufacturing methods. Laser powder bed fusion processing with selective laser sintering (SLS) is one common method in AM. One advantage of this and other AM methods is to fabricate components without assembling junctures which leads to optimized designs enabling weight reduction and improved performance. However, one limitation is the porosity of the resulting material due to, e.g., incomplete fusion or gas entrapment. Since those pores of different size, shape, and distribution, show a critical impact on the mechanical properties of the final component, the understanding or even controlling is essential. For efficient and cost-reduced analysis, computational approaches to analyze porous microstructures produced by selective sintering are presented in [1]. Therein, finite element meshes are generated based on 3D voxel data representing the material distribution within the porous microstructure. By variation of the voxel and/or mesh resolution, a detailed numerical investigation of the mechanical behavior of the material at the microscale is carried out. Moroever, the homogenized/effective properties and the computational treatment to obtain these are of interest, see [2]. By systematically varying process parameters, such as laser power and laser scan speed, their influence on the effective mechanical properties of the porous structures are examined. Dependent on the voxel and mesh resolution, high performance environments are required to determine the effective properties by numerical homogenization, since they result in large systems of equations (≈ 10^8dofs). The results and findings can be used in many ways to increase the efficiency of numerical simulations. Both for generating statistically similar representative volume elements, see e.g. [3], and as a training data set for teaching artificial intelligence models.