Establishing a Machine Learning and Internet of Things Learning Infrastructure by Operating Transnational Cyber-Physical Brewing Labs

The convergence of information technology with production technology, coupled with the escalating intricacy of production processes and products, is leading to new demands on the education of future engineers in mechanical engineering and related disciplines. New job profiles are arising and require interdisciplinary cooperation in the fields of information technology and the Internet of Things, machine learning and domain knowledge in order to enable data-based decisions for monitoring and improving products and processes in industrial production. The core of competence building has a strong focus on applying theoretical knowledge and information in order to make it tangible and thus enable people to learn. The contribution shows the conception and implementation of transnationally connected cyber-physical brewing labs, which were set up as learning factories for students and industry partners at TU Dortmund University and the University of Technology Sydney. The focus is on Industry 4.0 technologies, such as shared data space, condition monitoring of machines and assets in the Internet of Things, and the application of machine learning for product and process optimization. This article discusses the derivation of competence profiles, roles and the development of targeted theoretical and practical learning modules. It provides an overview of the use in various formats at both sites. The evolution of the brewing labs in existing research activities is also discussed. Finally, an outlook on future activities is given.