## 2 Proposed Workflow
### Image Rendering  
The automated workflow begins with the image rendering step, which generates the synthetic data for the subsequent network training step. Here, the synthetic training data comprise images generated from CAD models of the AM parts to be recognized. To generate the images, each CAD model is processed in the same manner using Blender, which is a free computer graphics tool. First, the CAD model is loaded from an STL file as part of a rendering scene. Then, the model's surface appearance is recreated using a material source image. Note that this material image must be captured manually once for each printed material. Afterwards, a noise texture is added to simulate possible irregularities in the appearance of the surface. Such irregularities are frequently caused by the fabrication process itself or by post-processing steps, e.g., sandblasting.  
After the part is loaded into the rendering scene, it is positioned on the ground plate by simulating drops from an elevated position. As a result, the initial orientation of the part is varied by fixed the degrees around the two axes making up an orthogonal triple with the direction of gravity. This is done to introduce variation into the positioning of the part. Then, the influence of gravity on the part is simulated to make it fall onto the ground plate. These falls are simulated to include viable part orientations on the ground plate under the influence of gravity, which allows for more realistic shadow rendering [26]. Once the part is positioned, images from different simulated camera angles are rendered using the Cycles rendering engine. Here, the cameras are equally distanced on a circle above the ground plate and angled toward the part. The images are rendered at a resolution of 256 \(\times\) 256 pixels and saved in JPEG format. The resolution differs from the final network input (224 \(\times\) 224 pixels) to allow for randomly cropping the image for enhanced variation in the training data. The following parameters are defined as the final workflow characteristics:  
* Physics simulation (gravity): On;
* Number of simulated drops per part: 64;
* Number of camera perspectives per part: 4;
* Total number of training images per part: 256.  
The influence of including simulated gravity in the rendering process and the number of camera perspectives on the classification accuracy is analyzed in Sections 3.2.2 and 3.2.3.  
Figure 3 shows the rendering scene and example synthetic images. The scene was designed to resemble the actual image classification scenery, which is referred to as the sensing area. It comprises a white ground plate and nine light sources. Here, one main light source (2.5 Watts) simulates a light-emitting diode (LED) panel that is mounted above the image sensing area, and eight secondary light sources (0.4 Watts) simulate the influence of atmospheric light and its reflection. These lights are positioned at equal distances in a circle above the ground plate. In addition, a Cartesian coordinate system is used to define  
positions in the rendering scene. Its origin is placed in the geometric center of the ground plate, and the \(z\)-axis lies parallel to the ground plate's normal vector.  
The entire rendering process is realized on a training instance using a Nvidia QUADRO RTX 4000 graphics processing unit (Nvidia Corporation, Santa Clara, CA, USA). Note that the computational resources required for the training instance are considerably greater than those for the inferencing instance. Thus, the training instance should be connected to multiple inferencing instances for network training to realize low system costs.