## 1 Introduction
[14] also reported the mechanical properties from tensile and flexural tests, compared three different reinforcement types with fiber volume fractions from 2% to 27%, and studied the influence of the build direction. The authors tested five specimens for each group of parameters. [PERSON] et al. [43] tested carbon fiber-reinforced nylon specimens as a benchmark to compare with their self-developed 3D printing system. The authors referred to a fiber volume fraction of approximately 35% (reduced by nylon layers) and more than three test specimens per conducted test. The measured flexural strength was 583 MPa \(\pm\) 19.7%. [PERSON] et al. [44] reported compression and flexural properties of carbon fiber reinforced nylon with varying fiber volume content (about 8%, 17%, 25% for compression and 17%, 32%, 49% for bending). Also, they varied the reinforcement (isotropic or concentric) and the printing direction. For each configuration, they tested two specimens. [PERSON] et al. [45] studied the effect of microscopic voids on mechanical performance using compression molding as a post-printing treatment. The authors reported tensile, flexural and shear properties of five specimens. The flexural strength of 3D printed specimens without post-treatment was 546.2 MPa \(\pm\) 3.6%. Despite the large amount of scientific work carried out to characterize the mechanical properties of CFC, the previous experimental results are hardly comparable and cannot be combined in a high-quality material model. Reasons for this are that the dimensions of test specimens and applied test standards differ and that the different studies used different fiber volume contents and reinforcement strategies. Moreover, each material model obtained from one individual study suffers from the small sample size used in this study, leading to an insufficiently accurate statistical description of the material parameters. In the context of material models for numeric simulation and optimization, this is referred to as incomplete knowledge in the model inputs or model parameter uncertainty [46]. Different types of uncertainties occur within the design process, classified as epistemic and aleatory uncertainties. For a given manufacturing system (e.g., CFF) in a certain state (e.g., defined process parameters), some uncertainties are reducible (epistemic uncertainties), while others remain irreducible (aleatory uncertainties). For instance, uncertainties in mechanical properties are reducible because the quantification of the variability depends on the sample size. A well-chosen sample size leads to a satisfying quantification of the statistical quantities, particularly the mean value and the standard deviation, which are used to evaluate the level of variability of the mechanical properties. In the end, the description of the material behavior of a numerical model is as accurate as the determined material properties. Therefore, there is a direct relationship between the accuracy of the material properties implemented in a material model and the numerical analysis result, which represents the behavior of the component under load. The predictive numerical model directly impacts the quality of the derived optimized components and influences how well the superior CFC material properties are exploited. Consequently, increasing the accuracy of the material model through a more accurate quantification of the mechanical properties leads to more reliable optimization results. This is quickly becoming a critical design and manufacturing issue as technology steadily progresses towards true 3D printing, where even more uncertainties occur. Additionally, numerical simulation plays an increasingly important role in the development process of lightweight parts, such as motorbike components [47]. Consequently, fewer prototypes are needed to perform the required mechanical testing, which shortens development times. More accurate simulation models are now under development to go even further and move directly from CAD design to end part manufacturing.