## 4 Discussion
Since the simulated data show inevitable offsets to corresponding measurements, for example, due to idealized and systematically erroneous material properties, a method for making the simulation data valid is also needed. Within this work, this was addressed with correction factors derived from five machine-made reference parts or from a comparative analysis of the extensive simulation and measurement data derived from them, respectively. Although one single reference part is sufficient in principle to access the systematic deviations from the simulation to the measurement world, more parts may lead to more reliable results. Most recently, further extensively characterized machine-made parts enabled the validation of an ART sorting model derived from synthetic data, before applying it to a series process. However, the required characterizations are connected with a significant workload and a need for suitable experimental technology. If additional a priori information, such as geometric data, should be included in a series quality evaluation, this will also require integrated measurement techniques. Thereby, it must be ensured that all of the measurement data associated with a specific part are correctly merged, which can be a major challenge in a production line. Another important issue is drifting component structures, for example, due to tool abrasion or material batch influences, which occur in a series production. This may be solved either with adaption steps from time to time, for example, with correction factors that are specific for each material batch. Alternatively, training data and sorting algorithms that cover wide structural part variations, including systematic variations in the material properties, could be designed. However, these points are a general challenge for ART, and not particular to the use of synthetic trading data. The same holds true for the temperature effects or production-specific perturbations. We have also carried out an analysis showing that an ART defect detection can be implemented, based on synthetic training data that comprise good part conditions alone. The idea is to predict the eigenfrequencies of a part from its structural data with a model derived from synthetic training data. The test decision is subsequently made by an evaluation of the frequency predictions versus the measured ART data. The disadvantage is that a powerful prediction algorithm is required, as well as the full specific data about the structural parameters varying from part to part. This may be hard to achieve in practice, or even impossible due to the limited data availability. This is especially the case if not only a geometric characterization, but also a reliable analysis with regard to a complicated material variations from part to part is mandatory. Even if the materials were constant within a batch and if only large geometric scatter occurred, the question arises whether the geometry could be captured completely with sufficient quality during a production process. A high-performance surface scanning system would be imaginable to completely acquire non-parametric shapes. Unfortunately, this may be very time-consuming, making it unachievable within a high-capacity series production. In addition, there are likely other limitations that prevent a complete geometric characterization, such as a rough surface finish. Although the approach may ultimately be limited to special cases, it is connected to advantages. On the one hand, the required training data amount is reduced, because only the good training parts are involved. No complicated defect modeling, as addressed above, is necessary. On the other hand, the approach assumes no explicit defect training and is therefore universal with respect to any type of structural defect or anomaly. Despite all of the challenges, synthetic ART training data entail many benefits. Batch effects or constructive component changes are quite easy to adapt to the simulation data and no completely repeated training data collection is needed.