###### Abstract
The FEM data can be used, for example, for general purposes and for the evaluation versus measurements, or for sensitivity analyses with regard to geometry, material properties, or structural defects [8; 13; 27; 28]. FEM simulations can quantitatively match the eigenfrequencies measured with ART very accurately, provided that the mechanical component properties are precisely known and modeled. Furthermore, the resonance spectra shifts caused by defects or due to other component variations were examined. Some approaches for the algorithmic separation between these different effects derived from the FEM data were published in literature [29; 30]. Other papers focused on the eigenfrequency shifts calculated by extensive FEM studies related to the position of a small defect or stiffness change, respectively. Based on such FEM data, conclusions were drawn algorithmically about unknown defects from a given set of eigenfrequencies [31; 32; 33]. A simulation-based generation of synthetic ART training data was initially addressed in our previous publication, with an application focus not on defect detection, but on the correlation between the geometries and the eigenfrequencies of undamaged parts (some material, datasets, and essential facts are revisited here, without giving the reference designation each time again to avoid repeated citation) [34]. To summarize, the literature covers many ART application-oriented publications, but also addresses the challenges connected with an experimental generation of ART training data. The simulation techniques, such as FEM, were identified as sufficiently powerful to generate the eigenfrequency data, which can allow the development of various ART data evaluation strategies in general. Other aspects that are addressed, such as the need for a method to distinguish between the influences of defects from those of tolerable variations or the achievements towards an inverse defect detection considering various defect positions, are relevant points for this study. While existing works demonstrate the usefulness of simulated data for developing algorithmic approaches, there is no systematic approach to the application to sets of real parts. It is pointed out that further research is needed, for example, with respect to the effects such as random part variations, the offset of the simulation data to the measurement data, or other practice-relevant effects. In contrast, the strategy, which will be presented, covers all, undamaged and defective parts, tolerable part-to-part variations, and the fundamental deviations of the simulation data from measurements. Moreover, the procedure is not only described in detail or discussed theoretically. It is also verified during a fictitious application scenario, by means of machine-made model parts and ART measurement data. Figure 1 outlines our strategy for setting up any specific ART application (for defect detection) based on synthetic ART training data. The essential steps are:  
* **Step no. 1** is the generation of the simulation data representing the part type, possible defect type(s) of interest, or the magnitude and nature of the tolerable part-to-part variations in a realistic manner. This entails actual simulations, as well as data enhancement or scaling methods. Furthermore, an adaption of the simulated data to corresponding measurement data is needed to reduce any systematic deviation of the synthetic data versus the measurement data. This can be achieved from a few machine-made reference parts that are characterized experimentally and are then rebuilt in the virtual world;
* **Step no. 2** consists of finding a suitable ART sorting algorithm from the synthetic training data set. The sorting algorithm depends heavily on the target of an ART application, which here is defect detection. However, it does not need to be different from the procedure in the case of experimental training data;
* **Step no.