## 1 Introduction
### Approach and Big Picture  
The following approach is used to achieve the described aim and test the hypotheses. The basic idea is to use the behavior and quality of trained models to evaluate the feature sets used. The models will predict the decoring behavior with some degree of deviation. Varying the features selected from all the experimental data to train the model will result in varying deviations in the model prediction. This variation can be used to compare different feature sets for their information content. A feature set yielding a model with a comparatively low deviation can be seen as containing more relevant information than a feature set resulting in a higher error. By using a transparent machine learning model such as XGB, further indications of the importance of features can be generated. Hypotheses of which feature sets are more or less important to decoring behavior are formulated, and datasets are composed accordingly. In all cases, the model quality has to be ensured by optimizing the models and evaluating their robustness. Otherwise, an insufficiently trained or non-optimized model is just as likely as an insufficient feature set. The specific approach is summarized in Figure 4. First, the target feature has to be defined. The possible alternatives are the measured decored mass after each interval and the measured difference in the decored distance from both open sides of the rectangular tube after each interval. Being easier to understand and easier to measure consistently, the decored mass is defined as the target feature for the model training. Thus, the trained models will predict the decored mass after each interval. In step two, the ram-impact acceleration curves recorded with the laser vibrometer are converted into scalar features. Three approaches are used for this: Fast Fourier transforms (FFTs), mel frequency cepstral coefficients (mfcc), and a statistical evaluation of the signals in the time domain. All three are typical approaches to translating signal curves into features for machine learning methods. These characteristics are calculated individually for all ram impacts and summarized for the respective interval. Section 3.1 explains the exact calculations. In step three, the division into datasets takes place. The original dataset with all features is split according to the previously described hypotheses. The main reason for this division of the data into feature sets is that the features in the data overlap in their information content. For example, the sand-binder system's name indirectly contains the sand's name, the binder's name, the binder, the weight content of the binder in the mixture, and their specific properties. It makes evaluating specific features more complicated. The division into sub-datasets can reduce this effect but only negate it partially. The second reason is the number of datapoints. Using all features simultaneously results in a dataset with about as many or more features as datapoints. These kinds of sparse data can lead to more overfitting during training. It can also obstruct the model training if the input vector is overly large compared to the number of datapoints. The detailed data composition is explained in Section 3.2. Step four is the first round of model calculations. As mentioned before, ANN and XGB models are used. The used programming libraries are listed in _Used programming libraries_ of Appendix A. It is essential to try different model parameter sets and optimize the models for each dataset. Without this step, the achievable model quality may depend significantly on the chosen model parameters. Even with dataset-specific optimization, this is always the case to some degree and must be considered in the interpretation. The likelihood of overfitting is reduced by calculating each parameter set ten times with random initializations. The average of these ten calculations is used as the final model score for a given parameter set. The model quality metrics and their interpretation are explained in Section 2.2.