## 4 Conclusions  
Additive manufacturing (AM) is an attractive technology for the manufacturing industry due to flexibility in design and functionality, but inconsistency in quality is one of the major limitations that does not allow utilizing this technology for producing end-use parts. Mechanical properties are dependent on both processes and material parameters of an additive manufacturing process, but there is a limited number of studies that focus on the STL model properties and part positioning in the build chamber as features that can be used to describe and predict mechanical properties in addition to material and process parameters. Since both material and process parameters were kept the same for the two builds analyzed in this study, this information is neglected, and the main focus is on the analysis of build layout. From Figures 4-6, it can be seen that the repeatability of the two builds is good, and this means that control of material properties can help to avoid inconsistency in the quality that usually appears from build to build. However, distributions in the ZYX orientation for Tensile Modulus and Nominal Stress are wide, which has not been reported earlier, and therefore, this issue should be analyzed in more detail in another article. Figure 12: Relative importance of the features based on the specimen orientations (**a**) Elongation at break—Angle orientation. (**b**) Elongation at break—All orientations. When it comes to the prediction of mechanical properties by using traditional methods like linear regression, it can be seen from Tables 5-7 that prediction accuracy for linear regression (except for Nominal Stress and Elongation at break in Angle orientation) is low, and therefore more advanced techniques need to be used. Since machine learning techniques have also been widely used in traditional manufacturing [32; 33; 34; 35], these methods were applied in this study to develop non-linear prediction models. These techniques are chosen for the reason that machine learning methods have an ability to find hidden patterns in the data that could be structured or unstructured (which is not possible with classical methods like DOEs), and then use those patterns to make predictions for unknown problems [9]. Therefore, data presented in different studies can be used together with data collected from practical experiments. Another benefit of using machine learning techniques lies in the long-term use of these models. In other words, the introduction of new data from each use of an additive manufacturing system will help to achieve continuous improvements of the model's performance. The models that are pre-trained based on the data collected from experimental work, like the one presented in this study, can be used as a starting point by companies, and then these models should be adjusted for specific a type of product. However, machine learning techniques require large datasets for better performance, and this trend can be observed based on the different model accuracies for orientation-based modeling and when all orientations are joined into one dataset. Models developed on the largest datasets (\"All orientation\" in Tables 5-7) have the highest prediction accuracy and, therefore, have potential to be used for the prediction of mechanical properties for similar types of parts. When it comes to the prediction of the mechanical properties for each orientation group individually, Tensile Modulus could be predicted only in the ZYX orientation, Nominal Stress could be predicted in the ZYX and Angle orientations, and Elongation at break could be predicted in Angle orientation. All other models require additional information about the material and increase the number of specimens that are analyzed. Since two builds were identical, there is a need for introducing more variations into the data such as; material properties, changing specimens' positions and orientations, producing specimens with other sizes and designs, and changing the values of STL model properties.