TY - JOUR A1 - Merz, Benjamin A1 - Nilsson, R. A1 - Garske, C. A1 - Hilgenberg, Kai T1 - Camera-based high precision position detection for hybrid additive manufacturing with laser powder bed fusion N2 - Additive manufacturing (AM) in general and laser powder bed fusion (PBF-LB/M) in particular are becoming increasingly important in the field of production technologies. Especially the high achievable accuracies and the great freedom in design make PBF-LB/M interesting for the manufacturing and repair of gas turbine blades. Part repair involves building AM-geometries onto an existing component. To minimise the offset between component and AM-geometry, a precise knowledge of the position of the component in the PBF-LB/M machine is mandatory. However, components cannot be inserted into the PBF-LB/M machine with repeatable accuracy, so the actual position will differ for each part. For an offset-free build-up, the actual position of the component in the PBF-LB/M machine has to be determined. In this paper, a camera-based position detection system is developed considering PBF-LB/M constraints and system requirements. This includes finding an optimal camera position considering the spatial limitations of the PBF-LB/M machine and analysing the resulting process coordinate systems. In addition, a workflow is developed to align different coordinate systems and simultaneously correct the perspective distortion in the acquired camera images. Thus, position characteristics can be determined from images by image moments. For this purpose, different image segmentation algorithms are compared. The precision of the system developed is evaluated in tests with 2D objects. A precision of up to 30μm in translational direction and an angular precision of 0.021∘ is achieved. Finally, a 3D demonstrator was built using this proposed hybrid strategy. The offset between base component and AM-geometry is determined by 3D scanning and is 69μm. KW - Laser powder bed fusion KW - Additive manufacturing KW - Hybrid repair KW - Machine vision KW - Image processing KW - Position detection PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-568583 DO - https://doi.org/10.1007/s00170-022-10691-5 SP - 1 EP - 16 PB - Springer AN - OPUS4-56858 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schmidt, Jonathan A1 - Merz, Benjamin A1 - Poka, Konstantin A1 - Mohr, Gunther A1 - Hilgenberg, Kai T1 - Surface structure analysis using visual high-resolution in situ process monitoring in laser powder bed fusion N2 - Parameter studies are a common step in selecting process parameters for laser powder bed fusion of metals (PBF-LB/M). Density cubes are commonly used for this purpose. Density cubes manufactured with varied process parameters can exhibit distinguishable surface structures visible to the human eye. The layer-wise process enables such surface structures to be detected during manufacturing. However, industrial visual in situ monitoring systems for PBF-LB/M currently have limited resolution and are incapable of reliably capturing small differences in the surface structures. In this work, a 65 MPixel high-resolution monochrome camera was integrated into an industrial PBF-LB/M machine together with a high-intensity LED (light-emitting diode) bar. Post-exposure images were taken to analyse differences in light reflection of fused areas. It is revealed that the grey-level co-occurrence matrix can be used to quantify the visual surface structure of nickel-based superalloy Inconel®939 density cubes per layer. The properties of the grey-level co-occurrence matrix correlate to the energy input and the resulting porosity of density cubes. Low-energy samples containing lack of fusion flaws show an increased contrast in the grey-level co-occurrence matrix compared to specimens with optimal energy input. The potential of high-resolution images for quality assurance via in situ process monitoring in PBF-LB/M is further discussed. KW - Additive manufacturing KW - Powder bed fusion KW - In situ monitoring KW - Image processing KW - High resolution camera PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-626685 DO - https://doi.org/10.1007/s40194-025-01955-1 SN - 1878-6669 SP - 1 EP - 15 PB - Springer Science and Business Media LLC AN - OPUS4-62668 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Merz, Benjamin T1 - Position Detection for Hybrid Repair of gas turbine blades using PBF-LB/M N2 - This poster presents a workflow for camera-based position detection of components within PBF-LB/M machines. This enables a hybrid repair process of highly stressed components such as gas turbine blades using PBF-LB/M. T2 - Kuratoriumsführung CY - Berlin, Germany DA - 21.06.2022 KW - Additive Manufacturing KW - PBF-LB/M KW - Position detection KW - Camera KW - Image processing PY - 2022 AN - OPUS4-56587 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -