## 3 Materials and Methods
### Simulation of Artificial Coating Defects as Part Shifting and Particle Drag  
As described in [24; 25; 35; 68; 69], it is hardly possible to intervene in any kind of process parameters of industrial PBF-LB/P systems, to adjust them, or to obtain information about exposure strategies. Consequently, it is difficult to simulate any defects in a reproducible manner during the running process without causing a complete process termination. The studies showed that with the EOS system, it is only possible to manipulate the process temperature before the start of the process in order to artificially cause a curling defect. Curling, which is the thermally induced in-process distortion of the free edges of build parts [73], can lead to the shifting of whole components if too pronounced. Depending on the size of the displaced parts and the current process progress, this can lead to an immediate process termination. However, as the height of curling can only be determined using complex measuring methods, as described in [24], the actual occurrence of this effect is subject to chance, making reproducible generation impossible. The effect of particle drag \begin{table}
\begin{tabular}{l l} \hline Laser type & CO\({}_{2}\) \\ Laser power & 30 W \\ Laser wavelength & 10.6 \(\upmu\)m \\ Scanning speed & 5 m/s \\ Exposed area (xy) & 200 mm \(\times\) 250 mm \\ Maximum part height (z) & 300 mm \\ Layer thickness & 50 \(\upmu\)m–200 \(\upmu\)m \\ Defined layer thickness & 100 \(\upmu\)m \\ Powder type & PA2200 nylon \\ \hline \end{tabular}
\end{table} 
Table 1: Parameters of EOS FORMIGA P 110 (EOS, Krailling, Germany). can also only be caused accidentally by a manual contamination of the powder. One of the main goals of this study was to artificially generate these defects efficiently, in order to generate as much data as possible from both the defect-free and defective process sections, and then train a custom CNN for automated defect detection. To determine the penetration depth of the foreign bodies at which the actual components are displaced from the powder bed, different wire thicknesses of copper and aluminum were applied to the coater blade, as shown in Figure 3, and the sintering process was then started normally. A total of seven different thicknesses from 0.3, 0.5, 0.6, 1, 1.5, 2, and 3 mm were applied to the coater. As well as the depth of penetration into the powder bed, the current state of the process was also important, making it difficult to make an explicit statement, so a penetration depth between 0.1 mm and 0.5 mm was defined for further investigations. After these initial trials, however, it quickly became apparent that a stationary assembly of a simulated defect on the coater blade was not practical for further investigation, as the defect would be continuously implied with each layer. In order to train a CNN effectively, sufficient data from both the correct and defective process steps must be available. When it comes to the real-time monitoring of the process, and especially the simulation of randomly occurring defects, the defects must not occur permanently. Based on these findings, a mechanical assembly was developed that is mounted on the coater blade and can be activated externally outside the machine. In order not to damage the machine, the activation of this defect simulation could only take place when the coater blade was in a certain area of the powder bed. To define the end positions of the coating blade, two limit switches have been fitted to the actuator cylinder inside the machine, as shown in Figure 4.  
Within this time window, the coating defect can then be randomly simulated, controlled via a Raspberry Pi. The number of new layers after which the defect occurs can also be randomized. The mechanical actuation of the defect on the powder bed surface was achieved using components made from stereolithography-produced high temperature material mounted on the coating blade to withstand temperatures of up to 170 \({}^{\circ}\)C during the ongoing sintering process.