In the metal processing industry, there are time-consuming repetitive tasks, e.g. checking parts if they are producible on a certain machine. In order to relieve the production manager and save time, this paper presents a self-learning system that carries out this task independently. Expert knowledge was collected, a synthetic data generator, a machine learning model based on a neuronal network for part classification as well as feedback modalities for experts were developed together with an Austrian sheet metal profile manufacturer. The solution was well accepted by the target group, but it became clear that it is important to integrate them into the whole development process. Furthermore, they can imagine that they trust the machine learning prediction after several weeks and thus the test of producibility could be automated. Tests on synthetic data showed a data collection period of approx. two years is necessary to provide satisfactory prediction accuracy if the model is trained from scratch. This time can be shortened by using pre-trained models.

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.

Maintaining healthy relationships and companionship are major challenges to prevent potentially harmful effects of loneliness and social isolation in older age. Therefore, to foster participation and user engagement of elderly people in online communication and entertainment the user experience of devices and services need to be improved substantially. This paper discusses a participatory design approach to design and implement a TV and tablet-based communications and entertainment platform called BRELOMATE. The results of a field study with 30 elderly people over five months shows that each users was online for 37.0 h and played the card game service for 23.3 h on average over the course of 8 weeks.