## 3 Results  
### Generated Experimental Database and Time-Series Processing  
In this sub-chapter, the exact features are shown and explained in as much detail as required for the scope of this work. For further details, the work of [PERSON] (2000) can be consulted. Following this work, the data are structured into sand-binder systems, specimen, and decoring intervals. System characteristics were determined for each sand-binder system. Each specimen was decored after a finite number of intervals. Each interval comprises between 10 and 80 ram impacts. It is fundamental for the discussion of the feature importance to understand the relation between the different kinds of features and the composition of the sand-binder systems. This paper uses 8 of the 17 sand-binder systems investigated in work [PERSON] (2000) for model training. Nine are discarded due to an insufficient number of datapoints. Table 1 shows the sand-binder systems used for model training and their compositions. R refers to \"reference binder\", and R_low to a binder with a lower modulus. The number of intervals and the number of specimens in each system are listed as well. In the last row, it is marked whether the previously mentioned extended features such as strain or temperature- depended strengths were recorded for this system. Figure 10 summarizes the data used to train the models. Corresponding to the experimental design described in Section 2.1, the parameters are grouped into six sources. Tables A1-A3 in Appendix A give a complete overview of all the parameters used in the training process. Additionally, they show their abbreviated names used in the SHAP plots. The target value predicted by the models is the decored mass in a single decoring interval, abbreviated as DM. Figure 9: Pseudo-code of the model training approach. The original system features include 3 categorical, 73 scalar quantities, and 1 time series. The categorical quantities describe the name of the sand used, the name of the binder used, and the name of the sand-binder system in the experimental setup. Two different binders and three different sands were used in the data used for model training. By using different binder percentages, eleven systems were combined from these components. These four features describe the composition of a sand-binder system and are therefore aggregated features. They are referred to as \"composition features\" as the measured physical features directly result from this composition. Of the scalar features, 41 are not used because they represent the standard deviation or the number of measurements for a measurement such as flexural strength. Five scalar features are not used because they represent an evaluation of the target feature. They correlate linearly to the target feature. Six other variables have only been measured for individual sand-binder systems and are discarded. Twenty-one scalar features remain and are used to train the models, seven of which are available for all sand-binder systems that are summarized as \"basic features\" and another fourteen that are referred to as \"extended features\". This division leads to two basic datasets. The first dataset contains a maximum number of datapoints and thus does not use the extended features. The second dataset uses the maximum number of features with all extended features but not all datapoints. Using both extremes makes it possible to evaluate the extended features without leaving the large number of datapoints available for the basic features unused. Further parameters were measured at the beginning of the interval in terms of mass distribution and decoring progress. They describe the initial state of the decoring interval. The value range and variation of the target value are essential to put the resulting model scores into context. Figure 11 shows the evaluation of the target feature for the two base datasets.