Implementing mixed reality in automotive life cycle engineering: A visual analytics based approach
Alexander Kaluza, Max Juraschek, Lennart Büth, Felipe Cerdas, Christoph A. Herrmann · Procedia CIRP · 2019
Supporting engineers with insights on product- or process related environmental impacts requires comprehensive interpretations of complex LCA models and results. Those rely on large amounts of data as well as different engineering models, both within the technosphere and at the interface to the ecosphere. Methods from visual analytics can support the interpretation of LCA results leading to a better integration of environmental constraints during decision-making. Interactivity is an essential element of the visual analytics process. In this regard, mixed reality technologies can increase interactivity and might lead to a better and faster understanding of the knowledge hidden behind the data. A case study applying a mixed reality solution within a visual analytics-based approach is introduced. It addresses the conceptual design stage in the engineering of future vehicle generations. Based on the case study, potentials and barriers of mixed reality in an automotive life cycle engineering context are discussed. Subsequently, an approach for evaluation with respect to capabilities in decision support is proposed.