Published in: Procedia CIRP: Volume 106
George Apostolopoulos; Dionisis Andronas; Nikos Fourtakas; Sotiris Makris
As market demands are characterized by more customized products with shorter lifecycles, it is obligatory for modern operators to manage recurrent product or manufacturing system changes. In contrary to previous years, adaptation to such changes prerequires memorization of more information, and familiarization with more complex systems and resources in a shorter period of time. This manuscript presents a novel operator training framework based on Augmented Reality (AR) technology. More specifically, intuitive instructions enhanced with machine learning-based physical object detection are used for making steeper learning curves and providing hands-on experience to operators. The implemented application also supports a walkthrough mode where users can get familiarized with Information and Communication Technologies (ICT) data streams besides fenceless Human-Robot coexistence in collaborative schemes. An automotive case study is used for evaluating the performance of the training framework through a Human-Robot Collaboration (HRC) assembly scenario.