## 4 Discussion
Although setting up the synthetic data requires knowledge and effort too, it may be less expensive and more flexible compared to the conventional experimental procedure. Furthermore, the simulation-based approach allows for the production of much larger training datasets, covering many thousands of parts. This may enable an ART application where too few experimental training data are available. Additionally, a large training datasets may enable the derivation of high-performance defect detection or sorting algorithms, based on artificial intelligence (AI) or machine learning methods, which require a very large data basis. Additional simulation data also allow the theoretical estimation of application-specific ART detection limits. In conclusion, we would like to state that using synthetic ART training data is very promising, although the full potential has not yet been exhausted. Within the study, a fictitious application scenario was addressed. Although the real measurement data from the machine-made parts were used, the demonstration focused on a simple defect type, only one defect per part was considered, and the material was assumed to be constant. Transferring the approach to a real series application is still an unsolved task that could not be achieved during the study. However, the demonstration of the model-like problem is an important intermediate step. A series transfer requires further research due to the challenges in terms of a realistic simulation modeling of the component structures, and complicated or multiple defects. Challenges, such as the availability and merging of measurement data from various measurement systems in a series production, production-specific perturbations, or batch effects, must be considered. In the long-term, a vision could be aimed at, in which all of the data and information accumulated during a whole production process are mapped by means of a digital part twin. Such a twin could serve for an improved part-structure modeling during subsequent simulations and could finally lead to more realistic synthetic ART training data. The twin data could also be evaluated in a multimodal way to optimize the sorting power of an ART algorithm. In addition, we see great potential in terms of modern sorting algorithms in general. AI or machine learning methods may permit further progress to a reliable ART. Such algorithms may be useful for the isolated evaluation of the acoustic data, as well as for the multimodal data analysis, including structural or process information. For studies on this, the extensive dataset generated during this work would be an excellent starting point. Furthermore, the AI methods may also be useful in the context of generating synthetic training data, for example, for the modeling of component variations or defect impacts. Approaches, such as GAN (Generative Adversarial Networks) [38; 39; 40], may be useful. A specific implementation of such a method is surely not straightforward and we did not research the possibilities and limitations regarding the special requirements for ART. Possibly, such methods can partly replace FEM as a method for data generation or enhance the FEM datasets to a much larger sample size. The very accurate adaption of simulated data to the measurements may be another aspect that could be addressed with such algorithms.