###### Abstract  
The Directed Energy Deposition process is used in a wide range of applications including the repair, coating or modification of existing structures and the additive manufacturing of individual parts. As the process is frequently applied in the aerospace industry, the requirements for quality assurance are extremely high. Therefore, more and more sensor systems are being implemented for process monitoring. To evaluate the generated data, suitable methods must be developed. A solution, in this context, was the application of artificial neural networks (ANNs). This article demonstrates how measurement data can be used as input data for ANNs. The measurement data were generated using a pyrometer, an emission spectrometer, a camera (Charge-Coupled Device) and a laser scanner. First, a concept for the extraction of relevant features from dynamic measurement data series was presented. The developed method was then applied to generate a data set for the quality prediction of various geometries, including weld beads, coatings and cubes. The results were compared to ANNs trained with process parameters such as laser power, scan speed and powder mass flow. It was shown that the use of measurement data provides additional value. Neural networks trained with measurement data achieve significantly higher prediction accuracy, especially for more complex geometries. D 202212 April 202214 April 202214 April 202214 April 202214 April 202214 April 202214 April 202214 April 202214 April 202214 April 202214 April 202214 April 20214 April  
predict target values based on defined input data [7]. The structure of neural networks consists of different layers: the input layer, the hidden layer or layers, and the output layer. Each layer consists of neurons that are connected by weights. The ANN can be designed associatively (the input data are related to the output data) or abstractly (predictions about unlearned samples) [8]. Existing studies for the application of ANNs in the DED process focus either on the prediction of optimal welding parameters [9; 10], process monitoring and the prediction of measurement data [11; 12], or the correlation of quality characteristics. There is currently high research potential, particularly regarding the correlation of component properties, as previous investigations only consider tracks and do not provide results on coatings or additive components. Moreover, process signals are not yet widely used to correlate quality characteristics using ANNs. [PERSON] et al. [13] show, in their work, that ANNs can predict the weld geometry based on laser power, feed rate, powder mass flow and focus position with an accuracy of 96.3%. [PERSON] et al. [14] achieved an accuracy of nearly 96% for the prediction of track width and burn-in depth with the use of an ANN. The track height could only be predicted with an accuracy of 72% [15]. Another approach for using ANNs in the DED process is the prediction of optimal welding parameters. [PERSON] et al. [16] demonstrated how the weld bead geometry and hardness can be used as input to predict parameter combinations of laser power, velocity and powder mass flow. The trained neural network achieved an accuracy of around 90%. The accuracy of ANNs always depends on the given input data that are used for the training and test runs. In particular, extrapolations of target variables outside the considered data range are almost impossible with the current models [17]. On the one hand, this is caused by the limited number of data points used and, on the other hand, it is caused by the static input parameters in the form of welding parameters. Studies show that ANNs can process all kinds of information as input data. Therefore, in addition to welding parameters, prepared process signals can be used to increase the performance and the robustness of the algorithms. This approach is already being practiced in other welding processes [18] and should now be transferred to the DED process.