###### Abstract
ART was used for the characterization of ceramic tiles regarding the effects linked with the mechanical properties and processing parameters [11]. Other studies dealt with the evaluation of the natural vibration behavior of some samples produced by selective laser melting [12; 13]. ART was also used for analyzing additive lattice structures [14; 15]. Swept-sine excitation was used to examine additively manufactured bulk parts, based on their natural vibrations [16], or to evaluate aerospace components, with respect to their metallurgical properties and defects [17]. A further work addressed microstructural variations or anomalies within cast iron test samples with the help of ART [18]. There are other studies that focused on composite materials by analyzing the eigenfrequencies for damage detection [19; 20]. Moreover, machine-learning algorithms were applied to ART data and used for the separation of healthy and damaged glass bottles [21], or for the classification of Euro coins [22]. There are also several studies dealing with ART-like approaches to various agricultural products, such as eggs or fruit [23; 24; 25]. ART is physically based on the free natural-vibration behavior of a body, which depends on its mass and stiffness distribution, or in engineering terms, on its geometry, the elastic constants of the material, and its density. In addition, the entire mechanical system, and thus the natural vibrations, are affected by various structural anomalies [26; 27]. Consequently, the eigenfrequencies of a component reflect its general mechanical properties, but also its specific expression in terms of geometric and material part-to-part variations or possible macroscopic defects, such as cracks or voids. From measured eigenfrequencies, it is not straightforward to make conclusions about the structural condition of a part, because different effects cause individual spectral shifts superposing on each other. Therefore, a reliable quality statement with ART is anything but trivial and requires a suitable algorithm to solve the inverse problem. This arises especially where large tolerable part-to-part variations occur or small anomalies are to be detected. Some ART companies advertise statistical algorithms that should be able to detect parts with defects or structural anomalies by outlier detection, assuming predominantly healthy parts. However, most of the ARTsystems or applications rely on an explicit application-specific calibration or on a training procedure built on empirical data, respectively. A corresponding training dataset must be generated beforehand by measurements of many representative parts. Thereby, the anomalies to be detected, ideally covering all of the possible expressions, as well as tolerable part variations, must be included. This results in the need for expensive or complicated reference methods to achieve the quality states of the training parts. A large number of parts is required, which may not be feasible for defective components from a series production with low rejects. There are also disadvantages from a practical point of view. Since component modifications reflect in the natural vibrations, they may require an update of the training data collection. In addition, the application of ART to a new part type requires the training data generation initially, which makes it usually not possible to use ART synchronously with a production launch. In summary, experimental ART training data generation may be inflexible, difficult, extensive, time consuming, and cost intensive. This study addresses the challenges connected with the experimental procedure by a strategy that focusses on the simulation-based generation of synthetic ART training data for ART defect detection. Although we found no work that is directly focused on the special topic of this study, other publications provided relevant findings. It is well known that the finite element method (FEM) can be utilized to simulate natural vibrations.