Auflistung nach Autor:in "Hebenstreit, Matthias"
1 - 2 von 2
Treffer pro Seite
Sortieroptionen
- WorkshopbeitragAn Industry 4.0 Production Workplace Enhanced by Using Mixed Reality Assembly Instructions with Microsoft HoloLens(Mensch und Computer 2020 - Workshopband, 2020) Hebenstreit, Matthias; Spitzer, Michael; Eder, Matthias; Ramsauer, ChristianEvery emerging technology raises the question which already existing processes and solutions can be replaced or adapted. Before implement these technologies they should be evaluated on their positive or negative impacts on already existing approaches. Furthermore, technical resources, organizational conditions and human factors have to be considered. In order to measure these impacts an appropriate industry scenario or use case should be defined. To foster the technology introduction in real world industry scenarios existing work processes were investigated and adapted. Prototypical implementations should give required insights to evaluate new technologies in defined use cases. In this work we introduce a Mixed Reality (MR) assembly instruction in manufacturing domain to support the worker in ensuring a zero failure culture. In an existing workstation of a scooter production line at the LEAD Factory at Graz University of Technology (TU-Graz) we introduced a emerging technology implemented on the Microsoft HoloLens. Based on this technology the existing textual instruction of one workplace was replaced by an interactive 3D assembly instruction. This paper describes the prototypical implementation of a MR assembly instruction with Microsoft HoloLens and summarizes the upcoming challenges as well as considerations and decisions during the implementation.
- WorkshopbeitragA Research Agenda to Deploy Technology Enhanced Learning with Augmented Reality in Industry(Mensch und Computer 2019 - Workshopband, 2019) Spitzer, Michael; Gsellmann, Inge; Hebenstreit, Matthias; Damalas, Stelios; Ebner, MartinTo apply Technology Enhanced Learning (TEL) with Augmented Reality (AR) in industry, a suitable methodology is necessary. This work focuses on how to deploy and evaluate AR learning scenarios in industrial environments. The methodology evolved within the two EU projects FACTS4WORKERS and iDEV40 and has been improved iteratively. The first step is to investigate the use case at the industry partner. Then the appropriate concept is defined. The next step is to develop a first prototype. This prototype is then improved during several iterations according to the feedback of the industry partner. When the prototype reaches an appropriate Technology Readiness Level (TRL), a final evaluation is carried out to verify the software artifact against the gathered requirements.