Context awareness in process monitoring of additive manufacturing using a digital twin

Wire Arc Additive Manufacturing allows the cost-effective manufacturing of customized, large-scale metal parts. As the post-process quality assurance of large parts is costly and time-consuming, process monitoring is inevitable. In the present study, a context-aware monitoring solution was investigated by integrating machine, temporal, and spatial context in the data analysis. By analyzing the voltage patterns of each cycle in the oscillating cold metal transfer process with a deep neural network, temporal context was included. Spatial context awareness was enabled by building a digital twin of the manufactured part using an Octree as spatial indexing data structure. By means of the spatial context awareness, two quality metrics—the defect expansion and the local anomaly density—were introduced. The defect expansion was tracked in-process by assigning detected defects to the same defect cluster in case of spatial correlation. The local anomaly density was derived by defining a spherical region of interest which enabled the detection of aggregations of anomalies. By means of the context aware monitoring system, defects were detected in-process with a higher sensitivity as common defect detectors for welding applications, showing less false-positives and false-negatives. A quantitative evaluation of defect expansion and densities of various defect types such as pore nests was enabled.

Local anomaly detection in hybrid rocket combustion tests

Local anomaly detection was applied to image data of hybrid rocket combustion tests for a better understanding of the complex flow phenomena. Novel techniques such as hybrid rockets that allow for cost reductions of space transport vehicles are of high importance in space flight. However, the combustion process in hybrid rocket engines is still a matter of ongoing research and not fully understood yet. Since 2013, combustion tests with different paraffin-based fuels have been performed at the German Aerospace Center (DLR) and the whole process has been recorded with a high-speed video camera. This has led to a huge amount of images for each test that needs to be automatically analyzed. In order to catch specific flow phenomena appearing during the combustion, potential anomalies have been detected by local outlier factor (LOF), an algorithm for local outlier detection. The choice of this particular algorithm is justified by a comparison with other established anomaly detection algorithms. Furthermore, a detailed investigation of different distance measures and an investigation of the hyperparameter choice in the LOF algorithm have been performed. As a result, valuable insights into the main phenomena appearing during the combustion of liquefying hybrid rocket fuels are obtained. In particular, fuel droplets entrained into the oxidizer flow and burning over the flame are clearly identified as outliers with respect to the main combustion process. Graphic abstract