TY - CONF A1 - Merz, Benjamin A1 - Mohr, Gunther A1 - Hilgenberg, Kai 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 - 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 JF - The International Journal of Advanced Manufacturing Technology (JAMT) 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 - CONF A1 - Merz, Benjamin A1 - Poka, Konstantin A1 - Nilsson, R. A1 - Mohr, Gunther A1 - Hilgenberg, Kai T1 - On the challenges of hybrid repair of gas turbine blades using laser powder bed fusion T2 - Lasers in Manufacturing (LiM) 2023 N2 - Additive manufacturing (AM) processes such as laser powder bed fusion (PBF-LB/M) are rapidly gaining popularity in repair applications. Gas turbine components benefit from the hybrid repair process as only damaged areas are removed using conventional machining and rebuilt using an AM process. However, hybrid repair is associated with several challenges such as component fixation and precise geometry detection. This article introduces a novel fixturing system, including a sealing concept to prevent powder sag during the repair process. Furthermore, a high-resolution camera within an industrial PBF-LB/M machine is installed and used for object detection and laser recognition. Herein, process related inaccuracies such as PBF-LB/M laser drift is considered by detection of reference objects. This development is demonstrated by the repair of a representative gas turbine blade. The final offset between AM build-up and component is analysed. An approximate accuracy of 160 μm is achieved with the current setup. T2 - LiM 2023 CY - Munich, Germany DA - 26.06.2023 KW - Laser powder bed fusion KW - Additive manufacturing KW - Hybrid repair KW - Position detection KW - High-resolution camera PY - 2023 SP - 1 EP - 9 AN - OPUS4-57836 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Merz, Benjamin A1 - Poka, Konstantin A1 - Nilsson, Ricardo A1 - Mohr, Gunther A1 - Hilgenberg, Kai T1 - On the challenges of hybrid repair of gas turbine blades using laser powder bed fusion N2 - Additive manufacturing (AM) processes such as laser powder bed fusion (PBF-LB/M) are rapidly gaining popularity in repair applications. Gas turbine components benefit from the hybrid repair process as only damaged areas are removed using conventional machining and rebuilt using an AM process. However, hybrid repair is associated with several challenges such as component fixation and precise geometry detection. This article introduces a novel fixturing system, including a sealing concept to prevent powder sag during the repair process. Furthermore, a high-resolution camera within an industrial PBF-LB/M machine is installed and used for object detection and laser recognition. Herein, process related inaccuracies such as PBF-LB/M laser drift is considered by detection of reference objects. This development is demonstrated by the repair of a representative gas turbine blade. The final offset between AM build-up and component is analysed. An approximate accuracy of 160 μm is achieved with the current setup. T2 - LiM 2023 CY - Munich, Germany DA - 26.06.2023 KW - Laser powder bed fusion KW - Additive manufacturing KW - Hybrid repair KW - Position detection KW - High-resolution camera PY - 2023 AN - OPUS4-57837 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -