## 1 Introduction
Currently, time, costs, and technical challenges for postprocessing these surfaces exceed the saved time and costs of the more convenient 3D printing process and restrict FFF from industrial application [24]. One possibility of overcoming these problems is to optimize the process beforehand by identifying appropriate printing parameters [25]. In order to do this, it is necessary to investigate the large number of individually adjustable printing parameters and their stochastic behavior in the FFF printing process. In the published literature, a number of studies can be found on optimization of 3D printing parameters. [PERSON] et al. [26] experimentally examined the effect of varying four of the significant process parameters, i.e., slice orientation, infill percentage, number of shells, and layer thickness on the energy and waste consumption when 3D printing with polylactic acid (PLA) materials. It was found that slice orientation had the greatest influence on the scrap weight, whereas layer thickness was the most significant parameter affecting energy consumption and production time. It was pointed out that there were trade-off effects of changing slice orientation on production time and material waste. This was because the orientation that gave a larger vertical cross-sectional area is associated with more waste but a smaller number of layers and lower production time. [PERSON] et al. [27] analyzed the correlation of surface roughness and energy consumption with layer thickness, printing speed, and infill ratio, for three desktop printers using PLA as printing material. A portable wattmeter and a laser microscope system were used to measure the responses. The results showed that energy consumption was linked to the printing time, which was influenced mainly by layer thickness. In addition, surface roughness was found to depend primarily on the layer thickness, one increasing with the other. Therefore, layer thickness is a conflicting factor, but the energy consumption can be reduced by increasing printing speed while maintaining a good surface quality. [PERSON] et al. [28] inspected the effect of layer thickness, printing speed, temperature, and wall thickness (contour thickness) on the surface roughness. Using analysis of variance, layer thickness and wall thickness were identified as critical factors, while printing speed and temperature seemed to have less influence. [PERSON] et al. [29] investigated the effect of layer thickness, support material, infill percentage, and part orientation on the surface quality. The study revealed a correlation between surface quality and increasing layer thickness due to the staircase effect, roughness increasing with step height. Roughness could be optimized by a part orientation of 90\({}^{\circ}\), and a reduction in model interior from solid to high density. [PERSON] et al. [30] studied the influence of material pigmentation, environmental humidity, and extrusion temperature on surface quality, dimensional quality, and mechanical strength. Only the extrusion temperature significantly affected surface and dimensional qualities. It was found that high extrusion temperatures increased dimensional deviations due to reduced viscosity. High extrusion temperature also increased the occurrence of bubbles, which negatively influenced the surface quality and increased dimensional deviations. [PERSON] et al. [31] investigated the influence of slice height, contour width, raster width, and air gap (between part and support structure), with three levels for each factor, on the material volume of an acrylonitrile butadiene styrene (ABS) test specimen using a FFF 3D printer. Regression analysis showed that the air gap was the most significant factor affecting the model material volume, followed by contour width, slice height, and raster width. [PERSON], et al. [32] studied the impact of five printing parameters on the mechanical properties, surface finish quality, and production time of additive manufactured PLA test pieces using FFF.