## 3 Results and Discussion
### Feature Importance for Prediction of Elongation at Break  
The features that described orientation angle had dominated over other parameters and has negatively influenced the model robustness for models developed to predict elongation at break, especially when all orientation groups were joined. Therefore, it was decided by the author to develop models based on the specimens' placements in the build chamber and STL models, and exclude orientation angle from the feature's list. This has resulted in lower prediction accuracy but has helped to overcome an issue of overfitting (memorized data instead of defined patterns and dependencies). However, it is still important to keep in mind that part orientation has the most significant impact on the elongation at break (see Figure 6, where raw values are in the range 1.917%-33.715%).  
Prediction of elongation of the break has higher prediction accuracy in Angle orientation (0.903 out 1) than in the ZYX orientation (0.67 out of 1), even though the number of data points for ZYX orientation is almost two times larger. While it seems that a number of data points are not an important factor for machine learning techniques, there are other reasons that explain the observed phenomenon. Development of a robust model with good generalization requires not only many data points but also features that influence the parameters which the model is predicting. Therefore, in future studies, there is a need for introducing material properties and additional information that will explain temperature distribution in the build chamber in a more detailed way. Additionally, it is important to analyze linear model performance in Angle orientation because this model also has prediction accuracy of 0.889, which could mean that Elongation at break has a linear correlation with the positioning of the specimens in the build chamber similarly to the Nominal Stress.  
Figure 11: Comparison of the correlations between Nominal Stress and coordinates in the X-axis (for all data) (**a**) Nominal Stress vs minimal coordinate in the X-axis. (**b**) Nominal Stress vs central coordinate in the x axis.  
Figure 10: Feature relative importance for Nominal Stress—All orientations.  
Figure 12 illustrates that the most significant parameter is the minimum coordinate in the X-axis. This feature is important for the model, but it does not mean that the minimum X coordinate influences the value of elongation at break. However, the positioning of specimens in the build chamber allows identifying changes in the temperatures in the build chamber, which influences mechanical properties, similar to other reports [12].  
Additionally, it is important to mention that the speed under which force was loaded on the specimens also has an impact on the values of elongation at break. Since the difference between values mentioned in EOS datasheets and obtained in the experimental work are similar, one can assume that this factor can be neglected.  
While Elongation at break for Angle orientation could be predicted based on four features (see Figure 12a, when all orientations are joined into one dataset, the number of important features for the model is different (see Figure 12b). As can be seen from Figure 12b based only on the positioning of the specimens in the build chamber, there is a high potential in predicting elongation at break. Similarly to Tensile Modulus and Nominal Stress, coordinates in the X- and Z-axes are defined as the most important ones. These results could be different for the AM machine, and therefore, in the future work, more data needs to be collected based on experimental work, which will be focused on the moving specimens within build chamber, with special attention to the features listed as the most important. Since the author could not find any similar reports in order to compare the obtained results, verification and validation of these results will be done in the future based on the additional experiments.