2023
,
Brünninghaus, Marc
,
Deppe, Sahar
To ensure a sustainable education for trainees, vocational training has to keep up with the elevated digitalization of manufacturing and work. Complicated technologies used in production systems shift the requirements of job training towards a more tech-centered preparation for future employments. Paired with increased diversity among trainees regarding cultural and educational backgrounds, solutions to accommodate both slower and faster learners are sought after. After an initial exemplification of challenges, that vocational training faces, we discuss the general usage of assistance systems in training applications, in order to better the respective results. For a comparison, we examine state-of-the-art digital learning platforms, i.e. assistance systems, that were developed to enhance vocational training. Typically, these platforms offer more or less individually tailored training plans and implement various technologies and interfaces to improve learning speed or quality. Based on the results of this comparison, we propose a new, mobile and user-adaptive assistance system “XTEND for education”, that involves all stakeholders of the training in the process and uses modern technologies to overcome the challenges of current vocational training. By building on top of node graph-based, dynamically generated assignments, AR-based inclusion of training material and equipment and a robust infrastructure to support it, the XTEND assistance system is shown to be capable of meeting requirements of vocational training in the realm of Industry 4.0.
2019
,
Dhiman, Hitesh
,
Büttner, Sebastian
,
Röcker, Carsten
,
Reisch, Raphael
Complexity is a fundamental part of product design and manufacturing today, owing to increased demands for customization and advances in digital design techniques. Assembling and repairing such an enormous variety of components means that workers are cognitively challenged, take longer to search for the relevant information and are prone to making mistakes. Although in recent years deep learning approaches to object recognition have seen rapid advances, the combined potential of deep learning and augmented reality in the industrial domain remains relatively under explored. In this paper we introduce AR-ProMO, a combined hardware/software solution that provides a generalizable assistance system for identifying mistakes during product assembly and repair.