The project “IDEA – Industrialisation of Digital Engineering and Additive Manufacturing” aimed to embed adaptive machining into the additive manufacturing (AM) process chain to support the post‑processing of AM parts. The BCT GmbH team focused on three core areas: acquisition and preparation of geometric data, integration of adaptive machining into production, and computer‑aided manufacturing (CAM) preparation for hybrid fabrication. The adaptive machining concept relies on real‑time measurement data taken from the part itself; accurate data are essential because they directly influence the machining outcome. By using high‑resolution optical measurements captured within the production line and referencing them with only a few points measured on the machining tool, the team was able to generate reliable geometric information for subsequent machining steps. Registration of the point clouds was performed with Iterative Closest Point (ICP) and a manually supported method, allowing the required data to be extracted either directly from the measured points or derived from the full cloud. The approach was validated on demonstrator parts, including a component supplied by Siemens Energy, confirming that the method can be applied in a production environment.

A key technical achievement was the development of new blending techniques that enable the removal of support structures without noticeable transitions. These methods allow the machining tool to follow the part geometry smoothly, preserving surface quality and dimensional accuracy. The team also introduced a novel approach for hybrid AM repair, where the missing volume of a worn component is calculated by comparing the CAD design with measured data. The resulting deformation field is applied to a closed CAD model to determine the fill volume. This technique was first tested on synthetic data, then extended with alignment functions, and finally applied to a real Siemens Energy part. Although the initial results are promising, further development is required to ensure robustness with imperfect real‑world measurements. Overall, the work demonstrates a significant step toward automating the post‑processing of AM parts, reducing manual intervention, and improving consistency.

Collaboration was central to the project’s success. BCT GmbH, based in Dortmund, served as the primary implementer and was funded by the German Federal Ministry of Education and Research (BMBF) under grant number 13N15002. The project ran from 1 May 2019 to 31 July 2022. Industry partners, notably Siemens Energy, provided demonstrator parts and practical feedback, ensuring that the developed solutions addressed real manufacturing challenges. Data exchange and development activities were conducted within a protected cooperation framework, allowing the integration of the new methods into BCT’s existing product portfolio. The project’s outcomes are expected to enhance the automation of post‑processing workflows in additive manufacturing, benefiting both BCT and its industrial partners.