## 1 Introduction
Thus, a purely part geometry-based approach to part recognition could be beneficial. One approach that does not require tags is the use of deep learning and neural networks, which have been studied extensively [15; 16]. A common application is image classification, where neural networks can be utilized to recognize the class of objects present in an image [17]. Thus, it is expected that neural networks can be used to recognize AM parts by classifying images of parts. Supervised learning techniques are frequently used to train a neural network for a given task, e.g., image classification. In supervised learning, labeled data, e.g., images, are required to adjust the network parameters (also  
Figure 1: Part-specific post-processing of AM parts in batches that requires part recognition. referred to as weights) to classify unlabeled data correctly [18]. Compared to conventional machine learning methods, such a learning process can be set up in an end-to-end structure, where only minimal human intervention is required [19]. In [20], it was demonstrated that neural networks can be employed to recognize AM parts. Here, different neural network structures for object detection were trained to classify and localize AM parts. The trained networks were then applied to recognize six different AM parts in images. However, this approach required capturing and labeling images of printed AM parts to train the network. As this process was performed manually, it is not feasible for the production of constantly changing AM geometries due to the high required effort. Acquiring labeled image data to train neural networks has been identified as a common challenge. Collecting and labeling training data can be time-consuming, and in some cases, it can be difficult to capture the required amount of training data [21]. Similar challenges, specifically for the application of neural networks in AM, were identified in [22,23]. The high effort and expertise required to create a training dataset and the lack of knowledge required to train a well-functioning neural network currently hinder application. To overcome these challenges in AM part recognition, the use of synthetic data to train neural networks for the classification of real images was mentioned in [20]. Here, training images can be generated as renderings from the AM parts' computer-aided design (CAD) files. Rather than capturing and labeling images, synthetic images and their corresponding labels can be created from digital 3D models. In addition, several previous studies have demonstrated that neural networks can be trained on synthetic images [24, 25, 26]. This method reduces the manual training costs significantly because it enables fully automated workflows to generate training data [27,28] without reducing the performance of the model [29]. CAD-based 3D models are a requirement in AM; thus, no additional costs are incurred to create them to generate training data. As a result, synthetic training data generation is suitable for AM processes. Previous studies have attempted to realize this approach based on synthetic training data to recognize AM parts. For example, neural networks for AM part recognition based on 2D data (images) and 3D data (point clouds and voxels) generated from CAD models were evaluated in [10]. Here, high part recognition accuracies were achieved; however, the authors did not address industrial application scenarios that require a generalized and optimized approach or specify the details required to reproduce their approach. In [9], a similar concept comprising a pipeline to train neural networks for image classification on rendered images was presented, which led to promising results in terms of recognizing AM parts with high accuracy. However, the authors did not describe all the necessary details, e.g., the required hardware or specific information about their evaluation methodology. A process based on synthetic training data used to classify and localize powder-covered AM parts was also developed in [12].