## 1 Introduction
In addition, vibration excitation by an airborne source or by dropping the component onto a stiff surface is possible [2]. The resulting vibration response is detected indirectly by a microphone via the emitted airborne sound or by contact detectors, which are, for example, connected to one of the part's supports. A direct contactless measurement by a laser Doppler vibrometer is also possible [12; 21], but this technology is especially useful as a reference method for experimental mode visualization [20; 21]. The frequency range of the detectors must match the frequency range of the relevant eigenmodes of the inspected part. Depending on the physical dimensions and the objective of an ART application, the frequencies of interest are either in the audible acoustic range or in the ultrasound range (compare resonant ultrasound spectroscopy, RUS, [6; 19]). Depending on frequency range, availability and application background, common experimental set-ups combine an impulse excitation with a microphone detection or a contact solution using a swept sine stimulation with a signal detection via the component supports [6; 20]. Independent of the sensor technology, the vibration signals are digitized, recorded, and processed using various methods for signal filtering, signal transformation, or extraction of acoustic characteristics. Based on the recorded signal or characteristics derived from recorded data, such as eigenfrequencies, amplitudes, or attenuation values, a part is considered to be OK/defective or it is classified with regard to its structural condition. Regardless of the algorithms used,each ART test decision within a series production is based on an application-specific calibration or on a training procedure built on empirical data. Such a data set was generated via experiment beforehand using a large number of representative parts. One challenge with ART is, however, that when inspecting for defects, each test result is to be understood in the sense of a conspicuity detection. Also, it is usually not possible to draw conclusions about the position, type, and size of a defect within a rejected part. Moreover, nominally identical test objects differ not only with regard to a possible defect or another target property of an inspection, but also as a result of tolerable part-to-part variations. These variations cause spectral shifts, which superimpose the acoustic test characteristics to be evaluated and thus have a negative impact on the reliability of ART. Within suitable training data, effects to be detected as well as tolerable variations should be included. For the experimental ART training data generation, a reliable, possibly expensive or complicated reference method for classifying representative serial parts is often necessary. Regarding defect detection and the collection of experimental training data, it can also be problematic that there is an insufficient number of representative defective parts, especially in a series production with low rejects. Furthermore, the influence of a defect on the eigenfrequencies depends not only on the type and size of the defect, but also on its position in a nonlinear manner [22; 23]. In summary, generating experimental ART training data can be very difficult, extensive, time consuming, and cost intensive. This also entails disadvantages of ART with respect to design adaptations. Since even slight component modifications reflect in the natural vibration behavior, they require an update of the training procedure. If ART is applied to a new type of parts, data generation is required initially and therefore it is usually not possible to use ART synchronously with production launch. In the field of acoustic testing (or even acoustic vibrations), extensive published information is available. NDT approaches that focus on the condition analysis of machines or plants by means of recorded vibration signals that occur during operation are not in the focus of this work. In fact, we limit the scope to the resonant vibration behavior of parts that follows of an active external excitation.