## 1 Introduction
Here, a neural network was trained on renderings that included simulated powdering from AM part CAD models. With this method, the powdering was modeled using an artificial powder simulator and a physics simulation. During evaluation, high classification and localization accuracy was obtained. The evaluation was conducted using a test set comprising four distinct AM part geometries. The network structure evaluated by the authors was trained for 100 epochs, meaning that the entire training dataset was iterated 100 times during the training process, which is time-intensive. Brief training times are especially relevant in an industrial application where the part geometries vary in every production batch. Classification networks must be retrained for every batch with new part geometries in this scenario and training times must kept short to not exceed AM production requirements. Thus, methods with intensive training are infeasible for the recognition of AM geometries that change regularly. In summary, previous studies have demonstrated the potential of using neural networks trained on renderings of AM parts' CAD models to recognize AM parts; however, the following limitations have been identified:  
1. High-performance hardware is used for the recognition process, which is not applicable for most AM service providers due to high investment costs [13]. 2. The computational resources required for data generation and network training are relatively high. This results in limited applicability in industrial settings where AM parts requiring recognition can vary daily, leading to training data generation and network training in high frequency. For this, data generation and training times must not exceed print times and be minimized for efficiency. 3. The processes required to generate training data and train neural networks and the required recognition hardware were not fully disclosed in the previous studies [9; 10]. Thus, it is unclear if these previous methods are generalizable and would perform well in an industrial application with high variation in part geometries. To overcome these limitations, in this study, an automated generalized workflow for visual AM part recognition using low-cost hardware is fully disclosed, optimized, and evaluated in an industrial case study. The focus is thereby set on further closing the gap for widespread industrial applicability in non-serial production, where each new post-processing batch potentially requires the training of a new network due to constantly changing part geometries. Training data generation and network training must therefore be generalized. This is opposed to serial production, where part geometries stay constant and only one recognition network could be trained and optimized for one specific set of parts. An overview of the proposed workflow is shown in Figure 2. The proposed workflow is based on STL format CAD models from which images of parts are rendered. These synthetic images are used in a network training step to create an image classification network, which is then utilized in the final step to recognize AM parts automatically. In this paper, a detailed description of the proposed workflow for automated part recognition is provided. Here, the focus is industrial applicability with low-cost hardware; thus, the workflow is fully automated and generalized. In other words, no neural network training expertise is required to train networks for new AM part geometries. In addition, the proposed workflow is optimized in terms of computational resources using a specifically created test set of 30 distinct AM parts. This optimization allows for network training during the printing process and economic use of the workflow in distributed production sites. Real-time part recognition is made possible by deploying a neural network on a low-cost single-board computer. Finally, an industrial case study involving 215 distinct AM part geometries and 519 AM parts in total is conducted to evaluate the generalized workflow.