DATA MANAGEMENT AND ARTIFICIAL INTELLIGENCE APPROACHES FOR DATA-INTEGRATED COMPUTATIONAL DESIGN, SIMULATION, AND FABRICATION
The overarching goal of this project is to develop novel methods to capture and leverage various forms of data to enable co-design between different phases of building individual fiber-based modules to be used as part of long-span buildings. Towards reaching this goal, the research project first focuses on data-based co-design for the structural long-span building design, simulation, and manufacturing of the aforementioned components. As of today, these different specialist divisions are typically performed sequentially and no data is systematically captured, transferred, or analyzed between these for (subsequent) optimization of the production process of similar modules.
In tight collaboration with further projects of this research network, the project will investigate how to model and integrate data about design criteria of the fabrication process and simulated material properties, structural data for transfer into instructions for fabrication, and fabrication constraints and fabrication data obtained through sensors. The research project will then explore how to use these data to bootstrap simulations and planning algorithms to improve the overall efficiency and quality of the fabrication process. This project will result in novel techniques for data processing coupled with artificial intelligence for efficient and effective co-design, applied in the context of constructing long-span buildings.
Prof. Dr.-Ing. Peter Middendorf
Institute for Aircraft Design (IFB), University of Stuttgart
Prof. Dr.-Ing. Alexander Verl
Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW), University of Stuttgart
- Gil Pérez, M., Zechmeister, C., Kannenberg, F., Mindermann, P., Balangé, L., Guo, Y., Hügle, S., Gienger, A., Forster, D., Bischoff, M., Tarín, C., Middendorf, P., Schwieger, V., Gresser, G. T., Menges, A., & Knippers, J. (2022). Computational co-design framework for coreless wound fibre-polymer composite structures. Journal of Computational Design and Engineering, 9(2), 310--329. https://doi.org/10.1093/jcde/qwab081
- Kaiser, B., Reichle, A., & Verl, A. (2022). Model-based automatic generation of digital twin models for the simulation of reconfigurable manufacturing systems for timber construction. Procedia CIRP, 107, 387--392. https://doi.org/10.1016/j.procir.2022.04.063
- Reichle, A., Ellwein, C., & Verl, A. (2022). Adaptive CAM planning to support co-design in the building industry. In N. Anwer (Ed.), Procedia CIRP (Vol. 109, pp. 78–83). https://doi.org/10.1016/j.procir.2022.05.217
- Ellwein, C., Reichle, A., Herschel, M., & Verl, A. (2021). Integrative data processing for cyber-physical off-site and on-site construction promoting co-design. Procedia CIRP, 100, 451–456. https://doi.org/10.1016/j.procir.2021.05.103