## 1 Introduction  
Additive manufacturing (AM), which is also referred to as 3D printing, has matured to become an established manufacturing process in various fields, e.g., prototyping or spare-part and end-user component production [1]. AM enables efficient low-volume production of highly mixed parts, e.g., customized industrial parts, including parts with complex geometries [2; 3], because AM does not require special tools or molds and allows for fast lead times and decentralized production at small and medium-sized fabrication sites. However, frequently used powder-based AM processes, e.g., laser powder bed fusion [4] and Multi jet Fusion (MJF) [5], often require extensive manual post-processing of nearly all parts [6; 7]. Parts must be cleaned of excessive powder and packaged. Optionally, they can undergo surface treatments or dyeing processes. Note that such post-processing steps and the printing process can be realized in batches because multiple AM parts can be printed or post-processed simultaneously [8]. However, not all AM parts are processed in the same manner; thus, batches frequently require sorting between each processing step. An example is packaging, where AM parts are sorted before being packaged for shipping to different customers. One critical subtask of part sorting is the recognition of parts. In [9], manual part recognition was identified as the primary time factor during the part sorting process, accounting for 40% of the total sorting time. Part recognition for sorting is particularly relevant for non-serial production of AM parts due to frequent changes in part geometries, e.g., for prototyping and customized parts. In addition, there is more variance in part-specific post-processing than in serial production. Figure 1 illustrates the steps in a conventional AM process from printing to shipping as a series of batch processes. If the upcoming process step differs among the AM parts in a single batch, e.g., the parts will be dyed different colors, they require recognition. AM part recognition is commonly performed manually, which is time-consuming, prone to error and cost intensive. Workers recognize each part individually using part lists that contain images of the parts' 3D models [10]. The time and effort increase when the number of AM parts processed in one batch is continuously maximized to ensure efficiency, for example, by optimizing the use of the build volume [11] or increasing the dimensions of the build chambers. When more parts are processed simultaneously, more parts require recognition, which increases recognition times because the parts must be recognized from a larger selection of parts. Thus, with increasing production volumes, the low degree of automation in part recognition can lead to a major bottleneck in the overall AM production process. This further hinders full end-to-end automation of AM post-processing [12]. Therefore, manual recognition of batches of AM parts presents a key challenge in the current post-process chain of powder-based AM processes. Consequently, it would be desirable to automate the AM part recognition process to reduce the time and labor costs of AM part sorting. To benefit from its potential, AM must be realized as efficient distributed production, e.g., for spare parts. This should be realized with low-cost systems to ensure low initial investment costs for new AM production facilities [13]. One solution that simplifies AM part recognition and allows for automation is tagging, which was reviewed in [14]. Tags can be optical or chemical markers, or geometric features, e.g., QR codes or barcodes, or integrated electronics, e.g., RFID chips. While these solutions allow highly accurate differentiation of different parts, they require additional effort in the part design process. Here, each AM part must be adapted with an individualized tag, which is not feasible for high volumes of changing part geometries because the process cannot be automated for all part geometries. In addition, not all tagging solutions are suitable for all part geometries.