## 3 Results and Discussion
### Feature Importance for Prediction of Tensile Modulus  
Prediction of Tensile modulus with model prediction accuracy higher than 0.8 out of 1 was considered as successful. However, when the author attempted to develop a model for each orientation separately, models for the XYZ, XY, and Angle orientations appeared to be unsatisfactory, and therefore it is not possible to extract a robust evaluation of which features are important. One of the reasons why this is not possible at this moment is related to the number of data points (which is much lower comparing with ZYX orientation and all data together). It is a well known fact that the more data points one has, the better the performance of machine learning techniques. Additionally, information about material properties is crucial for mechanical properties and adding this information in the future may improve models significantly. Since evaluation of the relative importance of the features on Tensile Modulus is possible only for the ZYX orientation and without data separation on orientation-groups, namely All orientations, Figure 7 illustrates the differences observed for these two groups. According to the results shown in Figure 7a, one of the STL model properties, which is volume, is depicted as relatively the most significant feature for the non-linear model based on the gradient boost regressor in the ZYX orientation. Since it is known from EOS that volume and surface parameters help to define energy concentration for a specific part, more energy will be concentrated in the build chamber where a part is placed. Thus, parts with larger values of volume (which means that the part is larger) will have larger energy concentration in the build chamber. In addition to this feature, part placement will also play an important role. Since it was already previously reported that in polymer powder bed fusion system temperature distribution within a build chamber is different, and also influences the mechanical properties [12; 20], it can be assumed that coordinates in the X, Y, and Z-axes help to identify regions where specimens are placed. Besides, in combination with the volume parameter, they provide a much better description of energy concentration and temperature distribution in the specific areas of the build chamber. In addition, machine learning techniques have a unique possibility of determining hidden patterns between feature and output, which are not visible to the \"human eye\", and therefore can provide better results. In order to better understand why coordinates in the X and Z-axes are listed in the top 5 of the most important features for the ZYX orientation, visualization of the Tensile modulus as a function of X and Z coordinates is needed. As Figure 8 (sum of relative importance for all features is equal to 1) shows, when central coordinates in the X-axis are larger than 250 mm, the tensile modulus has the lowest values. This indicates the correlation between coordinates in the X-axis and Tensile Modulus. Since part positioning in the build chamber is always described as a combination of all three axes, the coordinates in the Y and Z-axes provide additional information on how the Tensile Modulus changes depending on the position in the build chamber. However, when all orientations are analyzed as one data set, the list of the most important features differs from the one proposed for the ZYX orientation. Another STL model property is listed as relatively the most important, which is surface, and as it has been already mentioned earlier, the surface feature, similarly to volume, describes in more detail the energy concentration for a specific part, and thus contributes to the description of temperature distribution for a specific area in the build chamber. The coordinates follow the surface feature but in a different order. While minimal coordinate X is second in the list, its relative importance is a bit higher than for the ZYX orientation. Additionally, maximal coordinates in the Z and X-axes are important to the same extent.