TRR 277 - Transregio AMC: Additive Manufacturing in Construction
The Challenge of Large Scale
The SFB/Transregio TRR 277 investigates Additive Manufacturing (AM) as a digital manufacturing technology for the construction industry in an interdisciplinary, cross-location research project. In AM, components are produced solely through digitally controlled, layer-by-layer material application — without moulds or forming processes. This marks a paradigm shift away from traditional, craft-based construction, whose stagnating productivity and simple component designs lead to inefficient material use and significant CO2 emissions.
TRR 277 explores the fundamentals for implementing Additive Manufacturing in Construction (AMC), which enables high design freedom and resource-efficient material use. Realising this potential requires structural design, material behaviour and manufacturing processes to be rethought and made to interact. Three challenges remain in transferring AM to construction: scaling up to building size, achieving the material and process diversity that complex building requirements demand, and delivering the necessary individualisation and flexibility. These give rise to research questions on materials, process engineering, control, modelling, design and construction, addressed by teams from civil and mechanical engineering.
Two research approaches guide the work programme:
1. Combinations of materials and processes. Material and process coordination are treated as inseparable units. Project area A researches innovative material–process combinations and brings them to a new logic of form, investigating structural design, material behaviour and manufacturing as one integrated framework rather than restricting work to individual materials or processes. Project area B ensures robustness and full automation by providing feedback and numerical simulation capabilities to the A projects.
2. Seamless digitalisation in the building industry. End-to-end digitisation is crucial for introducing AM in construction. Project area C, "Design and Construction," researches the digital interfaces to upstream planning and downstream construction processes from the outset. The interaction between digital models and physical objects is the methodological link of TRR 277 and the basis for networking areas A, B and C — realised through the continuous production of large-scale demonstrators and their digital twins.
TU Braunschweig and TU Munich bring years of experience in interdisciplinary, cross-location AM research. Their excellent research infrastructure and complementary expertise underpin the programme and promote both universities' strategic development. TRR 277 promises high national and international visibility and aims to contribute significantly to the digitalisation of the construction industry.
C04 - Integrating Digital Design and Additive Manufacturing through BIM-Based Decision Support and Digital Twin Methods
The project will contribute to closing the gap between digital design and additive manufacturing. The conceived Design Decision Support System (DDSS) will enable the identification of components suitable for AM technology, founded on a for- mal AM knowledge base. Methods will be developed for generating Fabrication Information Models (FIM) from Building Information Models (BIM). These methods will be based on graph theory and algorithmic geometry. By incorporating manufacturing information from robots and sensors, methods for creating digital twin representations reflecting as-built geometry and properties are developed. The approaches are based on the concept of multi-LOD BIM ensuring the consis- tency between multiple levels of detail.
The proposed Design Decision Support System (DDSS) is founded on a knowledge base formalising the capabilities and boundary conditions of individual AM processes.
Methods
- Development of a multi-scale geometric model for AM in construction
- Combination of B-Rep and V-Rep as well as semantic modelling for multi-LOD BIM.
- Combination of graph transformation with algorithmic geometry for automated FIM derivation.
- DDSS: Knowledge representation based on graph theory and ontological descriptions.
- Usability experiments with potential users for validating the DDSS methodology.