## 2 Materials and Methods
### Conditioning of Specimens and Tensile Testing  
According to the DIN EN ISO 527-1, tensile testing was performed on universal test machine Zwick Z250 with one-week conditioning in the climate chamber at 70 \({}^{\circ}\)C and 62% RH. This is accelerated conditioning, which results in the same moisture content as mentioned in ISO 1110. In addition to one week of conditioning, specimens were kept for 1-2 days in the climate chamber at 23 \({}^{\circ}\)C and 50% RH before testing. The Zwick Z250 machine was loaded with 2.5 kN cell. The specimens were mounted in wedge grips with grip to grip distance set to 55 mm, and an initial gauge length of the extensometer of 25 mm. Application of Machine Learning Techniques to Predict Mechanical Properties without Prior Knowledge about Material Properties (PA)  
The successful prediction of mechanical properties of fabricated parts is an important factor of future adoption of additive manufacturing to produce end-user parts. The high-quality requirements and need for consistency are primary issues that have not been fully addressed yet. Typically, mathematical models need to be developed to make prior analysis of the mechanical properties possible. However, the complexity of the AM process leads to the simplification of the issue in one area in order to describe another one mathematically. Figure 3: Schematic visualization of parts’ orientations and dimensional features (where \(t\)—thickness, \(w\)—width and \(L\)—length). Therefore, another approach is required for obtaining robust predictions that will be less sensitive to unknown noise. Since there are examples of successful application of machine learning techniques in traditional manufacturing [32; 33; 34; 35], these methods could be used to predict mechanical properties with limited information about the AM process itself. Since the prediction of some output corresponds to regression task, linear regression models are compared with Gradient Boosting Regressor (GBR), Decision Tree Regressor, and AdaBoost Regressor machine learning techniques. The Python programming language was used to program these algorithms with the help of Scikit-Learn libraries [37], which consist of preprogrammed methods needed for this work. One of the advantages of using such techniques is a possibility to overcome the noise present and find patterns in the data that could not be identified with a \"human eye\". However, these methods require a large number of data points for better performance. An additional challenge is a choice of correct techniques for the assigned tasks. In this work, the determination coefficient and mean square error (MSE) were considered as performance metrics for methods comparison. However, machine learning techniques often work as black boxes and models cannot be presented mathematically except for a description of the model's architecture. An additional requirement to the methods was the presence of a feature importance attribute. This information is needed for better understanding which features play important roles in the model development to estimate Tensile Modulus, Nomial Stress, and Elongation at break (or identify features that are in some way correlated to the mechanical properties). Short Introduction to the Machine Learning Techniques Used  
**Decision Tree Regressor** is a recursive algorithm that splits data into smaller subsets (separate classes) in order to form a tree, and it is important to choose the correct metrics for the best data split and to determine when a tree node should become a terminal. Building a decision tree for regression is similar to classification, but the main difference is in using different metrics for the evaluation of the quality of the split (reduction of variance for regression task), and a leaf node is defined by numerical output. Optimization of these metrics will lead to the more stable performance of the algorithm. However, when it comes to analysis of the large amount of data, this method has issues with scalability, stability, and robustness [29; 38].