## 3 Results and Discussion
### Additional Value of Process Data  
Based on the previous results, one ANN based only on process parameters and one ANN based on the selected features were created, trained and validated for each of the different geometries. A total of 20 training and test runs were executed for each ANN. The accuracies of the individual runs can be taken from Figure 4.  
In a single run, the prediction accuracy of an ANN trained with a small data set is not very meaningful due to a possible unfavorable distribution of the training and test data. The average accuracy after multiple runs reduces the risk of a falsified statement about the network's prediction accuracy.  
Figure 4: Accuracies and average values of the runs for each geometry.  
In general, the accuracies are below the values of similar publications in the literature [9; 10; 11; 12]. There are two main reasons for this. Firstly, previous research usually uses continuous parameters as target values. In most cases, the effects of process parameters on bead width and bead height are predicted. The prediction accuracy calculation for continuous target values is generally performed by evaluating the relative error. Depending on the process, the kind of target value, and the used data set, these deviations are never large so that the general accuracy of the ANN is correspondingly high [13]. Secondly, it must be noted that the previous studies consider only weld beads. There is no transferability of the results to additive components. The quality characteristics of a weld bead can already be predicted well with a statistical test design [31]. The categorization considered in this work is, therefore, much more complex. In such cases, there is only the possibility of being right or wrong. Apart from that, it is not considered that all wrong classifications were off by just one category. Furthermore, a categorical evaluation of produced parts can already cause discussions among different operators. While the criteria for the respective quality categories are defined in principle, such an evaluation is also always subjective.  
The accuracy of the ANN based on process parameters decreases significantly with the complexity of the geometries; while for weld beads an accuracy of 70% could be reached, the accuracy drops to 60% for cubes. It is assumed that the predictions become even less accurate for relevant additive components, which usually have much higher degrees of complexity. This is primarily caused by the fact that the varied process parameters do not contain sufficient information to make correct quality predictions. The build-up strategy plays an especially major role in additive manufacturing. Since the build-up strategy is not given to the network as an input variable, the information is missing to correctly predict the quality category. Simply adding new aspects such as the build-up strategy as new training parameters and including them directly in the training phase is not practical, because the additional effort and time taken to produce sufficient training data would be too high.  
The integration of measurement data is a practical way of providing the network with varying and flexible information about the build-up process. It has been shown that with the selected features, the quality of all the considered geometries can be consistently predicted with an average prediction accuracy of 80%. Using geometry-dependent selection of features from the measurement data, specific information on the individual building processes can be collected and made available to the ANN. This includes information about changes in the process, as well as information about the dimensional accuracy of components. This way, the ANN can recognize an unsteady process or a failed build-up.