TY - JOUR A1 - Jokisch, T. A1 - Gook, S. A1 - Marko, A. A1 - Üstündag, Ömer A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael T1 - Laser beam welding of additive manufactured components: Applicability of existing valuation regulations N2 - With additive manufacturing in the powder bed, the component size is limited by the installation space. Joint welding of additively manufactured parts offers a possibility to remove this size limitation. However, due to the specific stress and microstructure state in the additively built material, it is unclear to what extent existing evaluation rules of joint welding are also suitable for welds on additive components. This is investigated using laser beam welding of additively manufactured pipe joints. The welds are evaluated by means of visual inspection, metallographic examinations as well as computed tomography. The types of defects found are comparable to conventional components. This is an indicator that existing evaluation regulations also map the possible defects occurring for weld seams on additive components. KW - Weld imperfections KW - Additive manufacturing KW - Weldability KW - Laser welding PY - 2022 VL - 2 SP - 109 EP - 113 PB - DVS Media GmbH AN - OPUS4-56374 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Marko, A. A1 - Bähring, S. A1 - Raute, J. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - Transferability of ANN-generated parameter sets from welding tracks to 3D-geometries in Directed Energy Deposition N2 - Directed energy deposition (DED) has been in industrial use as a coating process for many years. Modern applications include the repair of existing components and additive manufacturing. The main advantages of DED are high deposition rates and low energy input. However, the process is influenced by a variety of parameters affecting the component quality. Artificial neural networks (ANNs) offer the possibility of mapping complex processes such as DED. They can serve as a tool for predicting optimal process parameters and quality characteristics. Previous research only refers to weld beads: a transferability to additively manufactured three-dimensional components has not been investigated. In the context of this work, an ANN is generated based on 86 weld beads. Quality categories (poor, medium, and good) are chosen as target variables to combine several quality features. The applicability of this categorization compared to conventional characteristics is discussed in detail. The ANN predicts the quality category of weld beads with an average accuracy of 81.5%. Two randomly generated parameter sets predicted as “good” by the network are then used to build tracks, coatings,walls, and cubes. It is shown that ANN trained with weld beads are suitable for complex parameter predictions in a limited way. KW - Welding parameter KW - Quality assurance KW - DED KW - Artificial neural network KW - Additive manufacturing PY - 2022 DO - https://doi.org/10.1515/mt-2022-0054 SN - 0025-5300 VL - 64 IS - 11 SP - 1586 EP - 1596 PB - De Gruyter AN - OPUS4-56278 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -