Abstract We describe two proof-of-concept approaches on the sonification of estimated operation states and conditions focusing on two scenarios: a laboratory setup of a manipulated 3D printer and an industrial setup focusing on the operations of a punching machine. The results of these studies form the basis for the development of an “intelligent” noise protection headphone as part of Cyber Physical Production Systems which provides auditorily augmented information to machine operators and enables radio communication between them. Further application areas are implementations in control rooms (equipped with multi-channel loudspeaker systems) and utilization for training purposes. As a first proof-of-concept, the data stream of error probability estimations regarding partly manipulated 3D printing processes were mapped to three sonification models, providing evidence about momentary operation states. The neural network applied indicates a high accuracy (> 93%) of the error estimation distinguishing between normal and manipulated operation states. None of the manipulated states could be identified by listening. An auditory augmentation, or sonification of these error estimations, provides a considerable benefit to process monitoring. For a second proof-of-concept, setup operations of a punching machine were recorded. Since all operations were apparently flawlessly executed, and there were no errors to be reported, we focused on the identification of operation phases. Each phase of a punching process could be algorithmically distinguished at an estimated probability rate of > 94%. In the auditory display, these phases were represented by different instrumentations of a musical piece in order to allow users to differentiate between operations auditorily.

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.