## 2 Methodical Approach
### Database  
In order to test and analyze the new approach, a large dataset with a sufficient number of simulations is necessary. Currently, only structural mechanics simulations with components are considered. The dataset includes already calculated simulations from previous publications [16; 43]. The general structure of the entire dataset is organized with the help of the Opitz coding system [44]. The first digit of the code was selected as the distinguishing feature and the aim was to cover the existing categories as broadly as possible. The result with the corresponding simulation parts is listed in Table 1. The variables representing rotatory components stand for the length (L) and the diameter (D), while for the non-rotatory ones the maximum dimensions are sorted in all three directions in space and defined with A as the highest value and C as the lowest value. In total, five components from different applications and domains are integrated into the entire dataset, all displayed in Figure 7. All simulations were calculated with parametric CAD models using Ansys Workbench (version 19.2 and 2021 R2). For generation of the results, parameter studies were performed with d-optimal experimental designs (DOE) for the different simulations [45]. After successful calculation of the FE simulation, an APDL script saves the results as a text file, including stresses, deformations, boundary conditions, and the general mesh. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Rotatory** & \multicolumn{4}{c}{**Non-Rotatory**} \\ \hline _LD \textless{} 0.5_ & _0.5 \textless{} \  
The simulations were built using real loading conditions (inliner frame or mountain bike rocker) or information from the literature (crankshaft [46; 47], brake lever [48], or car rim [49; 50]). An overview of the various components and simulation setups is shown in Figure 7, with more detailed information on the simulations listed in [16; 43]. For all simulations, the element size of the mesh, the load values, and the geometric variables of the component were used as parameters. These all affect the non-plausibility of FE simulations, and the goal is to detect these certain error sources. Since the focus of previous datasets was not differentiated by plausibility cause, the geometric non-plausibility cases were not taken into account in all parts. To close this gap and to test how the models behave with smaller datasets, supplementary studies focusing on geometric plausibility reasons were created for the vehicle rim and brake lever. An overview of the calculated simulations and the labeled results is given in Table 2. In total, over 63,000 simulations are in the dataset, corresponding to a storage requirement of almost 13 TB. Most simulations were calculated with the bike rocker and the smallest amount with the crankshaft. In the case of the vehicle rim and brake lever, the second study for the geometrical error cause is added below in brackets as it was calculated with a second DOE plan. The sum of the plausible and different non-plausible classes always exceeds the total number of simulations. This is due to the fact that several reasons for non-plausibility can exist per simulation. The table also shows that the geometrically non-plausible reason is the least represented in the data and accordingly forms an imbalanced dataset, which can thus lead to favoritism toward the majority class from the trained model. Labeling of each parameter study was performed by a combination of automatic (e.g., rule-based for high load values) and manual labeling. Figure 7: Overview of the different simulation setups. Dataset Preparation  
After description and explanation of the available simulation pool, the next step is to demonstrate conversion of the simulation setup and results into matrices. The idea behind the transformation and arrangement of the matrices is to generate the \"DNA of the simulation\" and represent the simulation setup and results.