Due to increasing digitalisation, it is possible to digitally map the production of sheet metal profiles from configuration to production. In our research project we design, implement and evaluate a knowledge- and rule-based system in cooperation with a sheet metal profile manufacturer using a modern production machine. One big challenge hereby is the data acquisition to perform a producibility assessment. In many cases only the positive (producible) production data is stored and negative data is discarded during the production process. In this paper, we present approaches to generate and collect negative training data for a machine learning approach using a data generator and feedback from manufacturing experts to perform a predictive manufacturing assessment.

Air traffic control officers (ATCOs) are specialized workers responsible to monitor and guide airplanes in their assigned airspace. Such a task is highly visual and mainly supported by 2D visualizations. In this paper, we designed and assessed an application for visualizing air traffic in both orthographic (2D) and perspective (3D) views. A user study was then performed to compare these two types of representations in terms of situation awareness, workload, performance, and user acceptance. Results show that the 3D view yielded both higher situation awareness and less workload than the 2D view condition. However, such a performance does not match the opinion of the ATCOs about the 3D representation.