The diAMpro project, part of the LuFo V.3 consortium, investigated the digitalisation and automation of the additive manufacturing (AM) process chain for powder‑bed fusion used by Premium AEROTEC Industry and Airbus Aerostructures. The core objective was to capture and visualise the data flows between the horizontal value‑chain units and to analyse the vertical processes in design, production and quality assurance for efficiency and digital readiness. A value‑stream‑mapping approach was used to create a digital twin of the entire AM workflow. This twin served as a basis for process analysis, optimisation and for the validation of new concepts in a real test cell at the Technology Centre Varel, where the developed manufacturing and quality‑control procedures were verified.

Technical results are organised into five main work packages. HAP 1 focused on controlling the AM process chain. It produced a digital twin that integrates existing development and production systems, enabling end‑to‑end visibility. HAP 2 addressed material flow; it introduced automated powder processing and handling systems for components, and performed experiments in the real AM test cell to confirm feasibility. HAP 3 delivered quality‑assurance solutions, including automated inspection workflows and data‑driven defect detection. HAP 4 developed simulation models for the additive manufacturing process. It produced an additive laser melting (ALM) process model, a heat‑treatment/HIP model, a simulation of ALM post‑processing, and a comprehensive end‑to‑end ALM process‑chain model. These models were validated against experimental data and provide a basis for deterministic cost prediction and optimisation. HAP 5 concentrated on AM‑friendly design. Generative‑design algorithms and workflow models were created to reduce manual design effort and to ensure data continuity through a model‑based development approach. The design automation pipeline includes automated component design, automated AM preparation, and global optimisation of AM parts.

The project also explored data‑management strategies, including the use of semantic database technology and a specific ontology for traceability reports, developed in collaboration with Synergeticon GmbH. Neural‑network‑based methods were employed to predict and optimise production costs, complementing deterministic simulation approaches. The digital twin and simulation tools enable early detection of process bottlenecks and support rapid iteration of design and manufacturing parameters.

Collaboration involved several Fraunhofer institutes—IGCV, EZRT, and IAPT—providing expertise in casting, composites, X‑ray technology, and additive production technology. The Airbus Stiftungslehrstuhl for integrative simulation, material development and processes (ISEMP) contributed to simulation and material modelling. APWORKS GmbH, a subsidiary of Premium AEROTEC, was responsible for the development of AI topics in HAP 1.4 and process simulation in HAP 4. Synergeticon GmbH supplied semantic‑database solutions, while Synera GmbH (formerly ELISE) supported the development of design algorithms. The low‑code platform from Synera was later licensed through Altair Engineering’s partner programme, ensuring cost‑neutral access for the project. Project leadership was provided by Airbus Aerostructures GmbH, with Matthias Radny as project manager, Martin Hanisch and Markus Feiler as co‑project managers. The project ran under the LuFo V.3 framework, funded by the German Ministry of Education and Research, and concluded with a report dated 23 June 2023. The outcomes of diAMpro are intended to be transferable to other development and manufacturing domains, positioning the project as a lighthouse initiative for digitalisation in additive manufacturing.