TY - JOUR A1 - Graf, B. A1 - Marko, A. A1 - Petrat, T. A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael T1 - 3D laser metal deposition: process steps for additive manufacturing JF - Welding in the World N2 - Laser metal deposition (LMD) is an established technology for two-dimensional surface coatings. It offers high deposition rates, high material flexibility, and the possibility to deposit material on existing components. Due to these features, LMD has been increasingly applied for additive manufacturing of 3D structures in recent years. Compared to previous coating applications, additive manufacturing of 3D structures leads to new challenges regarding LMD process knowledge. In this paper, the process steps for LMD as additive manufacturing technology are described. The experiments are conducted using titanium alloy Ti-6Al-4Vand Inconel 718. Only the LMD nozzle is used to create a shielding gas atmosphere. This ensures the high geometric flexibility needed for additive manufacturing, although issues with the restricted size and quality of the shielding gas atmosphere arise. In the first step, the influence of process parameters on the geometric dimensions of single weld beads is analyzed based on design of experiments. In the second step, a 3D build-up strategy for cylindrical specimen with high dimensional accuracy is described. Process parameters, travel paths, and cooling periods between layers are adjusted. Tensile tests show that mechanical properties in the as-deposited condition are close to wrought material. As practical example, the fir-tree root profile of a turbine blade is manufactured. The feasibility of LMD as additive technology is evaluated based on this component. KW - Laser metal deposition KW - Build-up strategy KW - Deposition rate KW - Additive manufacturing PY - 2018 DO - https://doi.org/10.1007/s40194-018-0590-x SN - 0043-2288 SN - 1878-6669 VL - 62 IS - 4 SP - 877 EP - 883 PB - Springer Berlin Heidelberg CY - Heidelberg AN - OPUS4-44868 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 JF - Welding and Cutting 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 - Jokisch, T. A1 - Gook, S. A1 - Marko, A. A1 - Üstündag, Ömer A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael T1 - Laserstrahlschweißen additiv gefertigter Bauteile: Einsetzbarkeit bestehender Bewertungsvorschriften JF - Schweißen und Schneiden N2 - Bei der additiven Fertigung im Pulverbett ist die Bauteilgröße durch den Bauraum begrenzt. Das Verbindungsschweißen additiv gefertigter Teile bietet eine Möglichkeit diese Größenbegrenzung aufzuheben. Aufgrund des spezifischen Spannungs- und Gefügezustands im additiv aufgebauten Teil ist jedoch unklar, inwiefern bestehende Bewertungsvorschriften des Verbindungsschweißens auch für Schweißnähte an additiv gefertigten Bauteilen geeignet sind. Dies wird anhand des Laserstrahlschweißens additiv gefertigter Rohrverbindungen untersucht. Die Schweißnähte werden mittels visueller Prüfung, metallografischer Untersuchungen sowie Computertomografie ausgewertet. Die festgestellten Fehlerarten sind vergleichbar zu konventionellen Bauteilen. Dies ist ein Indikator dafür, dass bestehende Bewertungsvorschriften die möglichen auftretenden Defekte auch für Schweißnähte an additiven Bauteilen abbilden. KW - Schweißunregelmäßigkeiten KW - Additive Fertigung KW - Laserstrahlschweißen KW - Schweißeignung PY - 2021 VL - 73 IS - 3 SP - 132 EP - 137 PB - DVS Media GmbH CY - Düsseldorf AN - OPUS4-53575 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Jokisch, T. A1 - Marko, A. A1 - Gook, S. A1 - Üstündag, Ö. A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael T1 - Laser Welding of SLM-Manufactured Tubes Made of IN625 and IN718 JF - Materials N2 - The advantage of selective laser melting (SLM) is its high accuracy and geometrical flexibility. Because the maximum size of the components is limited by the process chamber, possibilities must be found to combine several parts manufactured by SLM. An application where this is necessary, is, for example, the components of gas turbines, such as burners or oil return pipes, and inserts, which can be joined by circumferential welds. However, only a few investigations to date have been carried out for the welding of components produced by SLM. The object of this paper is, therefore, to investigate the feasibility of laser beam welding for joining SLM tube connections made of nickel-based alloys. For this purpose, SLM-manufactured Inconel 625 and Inconel 718 tubes were welded with a Yb:YAG disk laser and subsequently examined for residual stresses and defects. The results showed that the welds had no significant influence on the residual stresses. A good weld quality could be achieved in the seam circumference. However, pores and pore nests were found in the final overlap area, which meant that no continuous good welding quality could be accomplished. Pore formation was presumably caused by capillary instabilities when the laser power was ramped out. KW - Inconel 718 KW - Laser welding KW - Selective Laser Melting KW - Laser Powder Bed Fusion PY - 2019 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-489679 DO - https://doi.org/10.3390/ma12182967 SN - 1996-1944 VL - 12 IS - 18 SP - 2967, 1 EP - 15 PB - Multidisciplinary Digital Publishing Institute CY - Basel AN - OPUS4-48967 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 JF - Material Testing 2022 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 - TY - JOUR A1 - Marko, A. A1 - Bähring, S. A1 - Raute, J. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - Prognose von Qualitätsmerkmalen durch Anwendung von KI-Methoden beim Directed 10 Energy Deposition JF - Schweißen und Schneiden N2 - Dieser Beitrag enthält die Ergebnisse eines im Rahmen der DVS Forschung entwickelten Ansatzes zur Qualitätssicherung im Directed Energy Deposition. Es basiert auf der Verarbeitung verschiedener während des Prozesses gesammelter Sensordaten unter Anwendung Künstlicher Neuronale Netze (KNN). So ließen sich die Qualitätsmerkmale Härte und Dichte auf der Datenbasis von 50 additiv gefertigten Probenwürfel mit einer Abweichung < 2 % vorhersagen. Des Weiteren wurde die Übertragbarkeit des KNN auf eine Schaufelgeometrie untersucht. Auch hier ließen sich Härte und Dichte hervorragend prognostizieren (Abweichung < 1,5 %), sodass der Ansatz als validiert betrachtet werden kann. KW - Kl KW - Directed Energy Depositio KW - Qualitätssicherung PY - 2022 SN - 0036-7184 VL - 74 IS - 10 SP - 672 EP - 679 PB - DVS Media CY - Düsseldorf AN - OPUS4-56284 LA - deu 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 - Quality Prediction in Directed Energy Deposition Using Artificial Neural Networks Based on Process Signals JF - Applied Sciences N2 - The Directed Energy Deposition process is used in a wide range of applications including the repair, coating or modification of existing structures and the additive manufacturing of individual parts. As the process is frequently applied in the aerospace industry, the requirements for quality assurance are extremely high. Therefore, more and more sensor systems are being implemented for process monitoring. To evaluate the generated data, suitable methods must be developed. A solution, in this context, was the application of artificial neural networks (ANNs). This article demonstrates how measurement data can be used as input data for ANNs. The measurement data were generated using a pyrometer, an emission spectrometer, a camera (Charge-Coupled Device) and a laser scanner. First, a concept for the extraction of relevant features from dynamic measurement data series was presented. The developed method was then applied to generate a data set for the quality prediction of various geometries, including weld beads, coatings and cubes. The results were compared to ANNs trained with process parameters such as laser power, scan speed and powder mass flow. It was shown that the use of measurement data provides additional value. Neural networks trained with measurement data achieve significantly higher prediction accuracy, especially for more complex geometries. KW - DED KW - Artificial neural network KW - Process monitoring KW - Quality assurance KW - Data preparation PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-547039 DO - https://doi.org/10.3390/app12083955 VL - 12 IS - 8 SP - 1 EP - 13 PB - MDPI AN - OPUS4-54703 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 - Quality Prediction in Directed Energy Deposition Using Artificial Neural Networks Based on Process Signals JF - Applied Sciences N2 - The Directed Energy Deposition process is used in a wide range of applications including the repair, coating or modification of existing structures and the additive manufacturing of individual parts. As the process is frequently applied in the aerospace industry, the requirements for quality assurance are extremely high. Therefore, more and more sensor systems are being implemented for process monitoring. To evaluate the generated data, suitable methods must be developed. A solution, in this context, was the application of artificial neural networks (ANNs). This article demonstrates how measurement data can be used as input data for ANNs. The measurement data were generated using a pyrometer, an emission spectrometer, a camera (Charge-Coupled Device) and a laser scanner. First, a concept for the extraction of relevant features from dynamic measurement data series was presented. The developed method was then applied to generate a data set for the quality prediction of various geometries, including weld beads, coatings and cubes. The results were compared to ANNs trained with process parameters such as laser power, scan speed and powder mass flow. It was shown that the use of measurement data provides additional value. Neural networks trained with measurement data achieve significantly higher prediction accuracy, especially for more complex geometries. KW - DED KW - Artificial neural network KW - Data preparation KW - Quality assurance KW - Process monitoring PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-555063 DO - https://doi.org/10.3390/app12083955 SN - 2076-3417 VL - 12 IS - 8 SP - 1 EP - 13 PB - MDPI CY - Basel AN - OPUS4-55506 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Marko, A. A1 - Raute, J. A1 - Linaschke, D. A1 - Graf, B. A1 - Rethmeier, Michael T1 - Porosity of LMD manufactured parts analyzed by Archmimedes method and CT JF - Materials Testing N2 - Pores in additive manufactured metal parts occur due to different reasons and affect the part Quality negatively. Few investigations on the origins of porosity are available, especially for Ni-based super alloys. This paper presents a new study to examine the influence of common processing Parameters on the Formation of pores in parts built by laser metal Deposition using Inconel 718 powder. Further, a comparison between the computed tomography (CT) and the Archimedes method was made. The Investigation Shows that CT is able to identify different kinds of pores and to give further Information about their distribution. The identification of some pores as well as their shape can be dependent on the Parameter Setting of the Analysis tool. Due to limited measurement Resolution, CT is not able to identify correctly pores with Diameters smaller than 0.1 mm, which leads to a false decrease on Overall porosity. The applied Archimedes method is unable to differentiate between gas porosity and other Kinds of holes like internal cracks or lack of Fusion, but it delivered a proper value for Overall porosity. The method was able to provide suitable data for the statistical Evaluation with design of Experiments, which revealed significant Parameters ont he Formation of pores in LMD. KW - Laser metal deposition KW - Additive manufacturing KW - Density measurement KW - Porosity KW - Design of experiments PY - 2018 DO - https://doi.org/10.3139/120.111232 SN - 0025-5300 VL - 60 IS - 11 SP - 1055 EP - 1060 PB - Hanser CY - Berlin AN - OPUS4-47094 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Marko, A. A1 - Schafner, A. A1 - Raute, J. A1 - Rethmeier, Michael T1 - Relative density prognosis for directed energy deposition with the help of artificial neural networks JF - Material Testing N2 - Additive manufacturing, and therefore directed energy deposition, is gaining more and more interest from industrial users. However, quality assurance for the components produced is still a challenge. Machine learning, especially using artificial neuronal networks, is a potential method for ensuring a high-quality standard. Based on process Parameters and monitoring data, part quality can be predicted. A further advantage is the ability to constantly learn and adopt to slight process changes. First tests using artificial neural networks focus on the prediction of track geometry. The results show that even a small data set is enough to provide high accuracy in the predictions. In this work, an artificial neural network for the predictive analysis of relative density in laser powder cladding has been developed. A central composite experimental design is used to generate 19 data sets. Input variables are laser power, feed rate and powder mass flow. Cubes are built up where density is considered as a target value. Several neural networks are trained and evaluated with these data sets. Different topologies and initial weights are considered. The best network reaches a confidence level of around 90 % for the prediction of relative density based on the process parame� ters. Finally, the optimization of the generalization performance is investigated. To this purpose, methods of variation in error limit as well as cross-validation are applied. In this way, density is predictable by an artificial neural network with an accuracy of about 95 %. KW - Directed energy deposition KW - Artificial neural network PY - 2021 DO - https://doi.org/10.1515/mt-2020-0004 SN - 0025-5300 VL - 63 IS - 1 SP - 41 EP - 47 PB - DE Gruyter CY - Berlin AN - OPUS4-52690 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -