## 1 Introduction
### Aim of This Work  
Models can be categorized depending on the approach and the methods used. Figure 2 depicts a possible categorization of models [18]. [PERSON] [2] used heuristical and phenomenological approaches to build his theory of important parameters for decoring behavior and predict new sand-binder systems' decoring behavior. He collected an extensive amount of experimental data during the progress. This work aims to replicate these results with a purely statistical approach using machine learning methods and the collected experimental data. The new approach is evaluated by comparing which parameters are important to predict decoring behavior according to the machine learning models and according to [PERSON] [2]. This results in three corresponding research questions:  
* Is it possible to train robust machine learning models to predict the decoring behavior of sand-binder systems using the given data?
* Which features are important for the decoring behavior according to the machine learning models, and are those the same as described in the previous work?
* Is it possible to extract further, possibly new insights from the data using machine learning models?  
All three questions are tested by a forwards-backward approach, as shown in Figure 3. Machine learning models are trained and optimized using the whole experimental data or subsets thereof. Depending on the resulting quality and behavior of the trained models, a conclusion is drawn whether the used subset of data includes the necessary parameters or not. A similar approach was used in [19], where insights into the main parameters of an industrial hydrocyclone were determined.  
By optimizing the models, the likeliness increases that the differences in the model quality of the prediction for different feature sets result from the different feature sets and not from randomly better- or worse-fitting models. The second question formulates a general comparison. To be able to answer it, it is transformed into five testable specific hypotheses:  
* Categorical features such as the name of the experimental series contain information beyond the collected data, for example, a different room temperature level during the  
Figure 3: Using model quality and behavior to evaluate experimental data.  
Figure 2: Types of models [2]. Adapted with permission from Ref. [18]. 2019, Elsevier.  
decoring process or slight but systematical differences in the clamping of the specimen. Including these features in the model training will improve the model quality but reduce the interpretability due to the aggregation of information into a single variable.
* The acceleration data contain important information for predicting the decoring behavior. Including it in the model training will improve the model quality.
* Different processing methods of the acceleration data are differently suited for machine learning models.
* Including all features in the model training will reduce the model quality due to a high data complexity without more information than in a reduced dataset. Reducing the data complexity in multiple steps using a feature selection method will improve the model quality for each dataset. At a given threshold, the model quality will drop significantly and abruptly. All features included at that threshold can be considered significantly important for the decoring behavior.