Manufacturing companies are facing the challenge of producing highly variable products in ever shorter cycles. To manage this complexity, digital, interconnected, and adaptable automation systems are essential. Equally important is the ability to derive consistent and usable insights from large volumes of technical data — especially when this data is distributed across different tools, models, or departments.
Funded by the Federal Ministry for Research, Technology and Space (BMFTR), fortiss worked with its partners Bosch Rexroth, the German Research Center for Artificial Intelligence (DFKI), EAW, Festo, Fraunhofer IESE, HTW Berlin, Lenze, Objective Partner, PSI, RWTH Aachen, SMS group, and XITASO to develop an open, model-based software platform within the BaSys4Transfer project. This platform enables the transfer of Industry 4.0 technologies into industrial practice.
Building Digital Twins with Open Source
At the core lies the Eclipse BaSyx framework — an open-source platform for implementing Asset Administration Shells (AAS). AASs represent the digital twin of machines, components, or processes and provide a standardized data foundation for Industry 4.0. Thanks to the free and open implementation, particularly small and medium-sized enterprises benefit from the straightforward introduction of flexible, data-driven production architectures.
Under the lead of Andreas Bayha (Head of Competence Field Model-based Systems Engineering), fortiss researchers developed methods for automatically detecting discrepancies across data of different disciplines, tools, and data sources. This ensures consistency between AAS submodels and engineering models, enabling well-founded and precise engineering decisions. Based on this, plant architectures can be digitally evaluated, optimized, and quickly adapted to new products — reducing downtime and accelerating product launches.
Foundations for smart, data-driven engineering
BaSys4Transfer strengthens digital maturity in mechanical and plant engineering and demonstrates how research-driven approaches can be translated into industrial solutions. Reliable, interdisciplinary data management, standardized digital modeling, and automated validation and optimization of plant architectures enable faster decisions, better production adaptability, and lower failure risks. At the same time, the project establishes a foundation for further research in seamless, interdisciplinary systems engineering—across industries. The results are currently being prepared for broader industrial adoption and will significantly shape future research into digitalized, adaptable manufacturing systems.