## 3 Results
### From Knowledge Extraction to New Knowledge Generation  
Generating rich semantic structured MSE data with connected process graphs along process chains offers significant benefits. This data can be utilized to feed AI models, enabling new insights, optimization of production steps or mechanical properties, and per  
Figure 13: Visualization of the process chain corresponding to the object ARI_A16 with the help of the Visual Graph tool of GraphDB database engine [39]. forming reliable predictions. In this section, a Decision Tree Regression model is trained and validated to illustrate how the RDF graph created for the studied AlSi10 Mg in the form of chronologically linked process data can be exploited to find the optimal production parameters aiming to improve the tensile strength (\(R_{m}\)) of the investigated material. For this didactic purpose, the relatively simple Decision Tree Regression model (the _DecisionTreeRegressor model_ from the'sklearn.tree' module included in the'scikit-learn' Python package [43]) was selected. This model works by continuously splitting a training dataset based on selected feature variable values, resulting in a tree-like structure that performs predictions on the target variable(s). As a result, a trained model capable of assessing the impact of the included feature variables on the target is generated. The first step in the modeling procedure was to query from the knowledge base all the conducted tensile tests including the previous history information of each tested specimen (the chemical composition from the casting process and the undergone heat treatment steps with their corresponding process control parameters), as well as the resulting material tensile strength from the performed tensile tests. Secondly, the target and feature variables were selected, where:  
* \(R_{m}\) was chosen as the **target variable** or independent variable, to make predictions on;
* The magnesium weight fraction ('weightfraction_Mg[%]'), the annealing duration ('duration_annealing[min]'), the annealing temperature ('temperature_annealing[\({}^{\circ}\)C]'), the aging duration ('duration_aging[min]') and the aging temperature ('temperature_aging[\({}^{\circ}\)C]'), represent the **feature variables** or dependent variables expected to influence the target variable. In the next step, the retrieved dataset is randomly split into **training data** (70 % of the dataset) and **validation data** (30 % of the dataset). Then, the training data is used to train the Decision Tree Regression model, which is configured through the following hyperparameters:  
* \(\text{max\_depth}=3\), controlling the maximum depth of the tree;
* \(\text{min\_samples\_split}=6\), which determines the minimum amount of samples required to split an internal node;
* \(\text{min\_samples\_leaf}=3\), representing the minimum amount of samples required per leaf. In this study case, the model splits the **training data** into subsets applying the default criterion (the Mean Squared Error) by selecting a feature and a threshold value of the selected feature and searching for the split that minimizes the error of each subset. The splitting process stops once the maximum depth of the tree or the minimum samples per split is reached. The tree grows per split with newly generated branches, composed of nodes representing a decision (internal node) or a prediction (leaf nodes). Figure 14 displays the tree-like form of the fitted Decision Tree Regression model. The model predicts that the optimal production route to maximize the tensile strength of the cast AlSi10 Mg is achieved for an amount of Mg in the cast melt greater than 0.305 % and an aging duration greater than 120 min, where the prediction of the target variable results from averaging the tensile strength value of a data subset composed by four samples. It is observed that other included feature variables in the fitted model are not displayed in the tree; this is explained by the low-scored level of importance of these missing variables.