Patrick Ruediger-Flore

• This work presents a visual analytics-driven workflow for an interpretable and understandable machine learning model. The model is driven by a reverse engineering task in automotive assembly processes. The model aims to predict the assembly parameters leading to the given displacement field on the geometries surface. The derived model can work on both measurement and simulation data. The proposed approach is driven by the scientific goals from visual analytics and interpretable artificial intelligence alike. First, a concept for systematic uncertainty monitoring, an object-oriented, virtual reference scheme (OOVRS), is developed. Afterward, the prediction task is solved via a regressive machine learning model using adversarial neural networks. A profound model parameter study is conducted and assisted with an interactive visual analytics pipeline. Further, the effects of the learned variance in displacement fields are analyzed in detail. Therefore a visual analytics pipeline is developed, resulting in a sensitivity benchmarking tool. This allows the testing of various segmentation approaches to lower the machine learning input dimensions. The effects of the assembly parameters are investigated in domain space to find a suitable segmentation of the training data set’s geometry. Therefore, a sensitivity matrix visualization is developed. Further, it is shown how this concept could directly compare results from various segmentation methods, e.g., topological segmentation, concerning the assembly parameters and their impact on the displacement field variance. The resulting databases are still of substantial size for complex simulations with large and high-dimensional parameter spaces. Finally, the applicability of video compression techniques towards compressing visualization image databases is studied.