## 1 Introduction
### State of the Art  
The intention to support users in the application of FE software has been present in the academic landscape for a long time. A very early knowledge-based system was presented by [7] in the late 1980s. With the help of the SACON (Structural Analysis CONsultant) system, the FE calculation of Boeing 747 wings could be improved and access for less experienced engineers was facilitated. Another analysis tool that enhances FE simulation with knowledge-based engineering (KBE) was introduced by [8], with the aim of supporting set up of the FE simulation through knowledge databases. The system was demonstrated with aluminum draw bending components as an example. In addition to knowledge-based approaches, the use of Machine Learning (ML) techniques in combination with FE simulations has also become popular for facilitating FE simulation tasks. To improve material generation, an approach was presented by [9] that assists the modeling of materials through evolutionary polynomial regression. The approach was integrated into the existing FE simulation process and tested for linear elastic and elastic-plastic material models. The preparation of geometry, a sub-step of preprocessing, can also be optimized by Machine Learning methods. For example, [10] developed a method that detects different ribbon features in components, which enables high-quality meshing of these geometric features. In addition to the recognition, a decomposition into individual areas is also essential for generating a suitable mesh. Another preprocessing upgrade was created from [11], which describes hole detection for mold injection parts. The identified holes are removed from the parts in a subsequent step to facilitate and accelerate the meshing of the different mold components. Furthermore, a contribution dealing with the automatic geometric simplification of components was created by [12]. This approach relies on geometric primitives for segmentation and following morphological investigations of the component. The methods generate a simplified surface model for the simulation from the 3D volume model through dimensional reduction and fitting of the contact surfaces. The authors of [13] provided another contribution to the automation of the preprocessing process by building ontologies via text and data mining, which builds the foundation of the simulation model. In this context, standards or simulation reports serve as the source of the knowledge base and help to automatically set up the simulation. Depending on the analysis requirement, the simulation can be set up accordingly, such as by simplifying bolted connections as beam elements or by modeling them with their threat. Examples of further applications of Machine Learning in the environment of FE simulations are given in [14]. In addition to the listed application fields, other areas such as the efficient optimization of the simulation results and the evaluation of the geometry are also listed. The presented methods combine assistance to the user with the generation or evaluation of FE simulations. However, these methods [7; 8; 10; 11] only offer solutions for specific problems or use cases. Other approaches [9; 12; 13] rely on pre-existing knowledge but do not utilize existing simulations from older projects. This gap is filled by the projection method for simulation data developed by [15] and improved in [16]. The objective of this method is to utilize existing simulation data to train a Deep Learning (DL) model that is subsequently capable of classifying new simulations into the classes \"plausible\" and \"non-plausible\". The term \"plausibility\" was defined in the context of FE simulations by [15] as a simulation that does not contain obvious mistakes an experienced simulation engineer would recognize. These errors include incorrect loads such as unit errors for forces (e.g., kN instead of N), meshing of the work piece that is too coarse, or faulty geometry parameters for components.