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 U6 - 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 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 - 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 - Quality Prediction in Directed Energy Deposition Using Artificial Neural Networks Based on Process Signals 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 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-547039 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 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 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-555063 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 - CONF A1 - Müller, V. A1 - Marko, A. A1 - Kruse, T. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - Analysis and recycling of bronze grinding waste to produce maritime components using directed energy deposition N2 - Additive manufacturing promises a high potential for the maritime sector. Directed Energy Deposition (DED) in particular offers the opportunity to produce large-volume maritime components like propeller hubs or blades without the need of a costly casting process. The post processing of such components usually generates a large amount of aluminum bronze grinding waste. The aim of the presented project is to develop a sustainable circular AM process chain for maritime components by recycling aluminum bronze grinding waste to be used as raw material to manufacture ship Propellers with a laser-powder DED process. In the present paper, grinding waste is investigated using a dynamic image Analysis system and compared to commercial DED powder. To be able to compare the material quality and to verify DED process parameters, semi-academic sample geometries are manufactured. T2 - LiM 2021 CY - Munich, Germany DA - 21.06.2021 KW - Additive Manufacturing KW - Maritime Components KW - Powder Analysis KW - Recycling KW - Directed Energy Deposition PY - 2021 SP - 1 EP - 9 AN - OPUS4-54067 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 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 - 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 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 U6 - 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 - TY - CONF A1 - Raute, J. A1 - Jokisch, T. A1 - Marko, A. A1 - Rethmeier, Michael T1 - Untersuchung zum Elektronenstrahlschweißen heißrissgefährdeter Nickelbasis-Superlegierungen mittels statistischer Versuchsplanung N2 - Nickelbasis-Superlegierungen sind seit vielen Jahren in unterschiedlichen Industrieanwendungen im Einsatz. Aufgrund der großen Heißrissneigung ist das Schweißen dieser Werkstoffe jedoch bei einer Vielzahl von Legierungen problematisch. Neue Arbeiten auf dem Gebiet zeigen, dass entgegen den gängigen Theorien auch reduzierte Schweißgeschwindigkeiten eine Tendenz zur Verringerung der Rissneigung aufweisen. Bisher existieren jedoch kaum Erkenntnisse zum Prozessverhalten in diesem Parameterbereich. In dieser Arbeit wird daher der Einfluss der relevanten Prozessparameter beim Elektronenstrahlschweißen (EBW) auf die Nahtgestalt im Bereich geringer Vorschubgeschwindigkeiten untersucht. Auf Grundlage der gewonnenen Erkenntnisse soll ein Ansatz zum rissfreien Fügen von komplexen Nickelbasis-Superlegierung gebildet werden. Die praktische Umsetzbarkeit wird abschließend anhand einiger Probeschweißungen an einem besonders heißrissgefährdeten Werkstoff demonstriert. Um fehlerfreie Verbindungen zu ermöglichen, wurden zunächst die relevanten Parameter für die Einstellung von Nahtbreite, Einschweißtiefe, Aspektverhältnis und Nahtfläche anhand einer Versuchsreihe mit 17 Blindschweißungen auf einer 12 mm dicken Platte aus Inconel 718 bestimmt. Die genaue Beschreibung des Einflusses der als signifikant identifizierten Faktoren erfolgte über die Anwendung einer Regressions- und Varianzanalyse. Die Ergebnisse zeigen, dass die Einschweißtiefe, die Nahtbreite, das Aspektverhältnis sowie die Nahtfläche vorrangig über den Strahlstrom, die Fokuslage sowie den Vorschub beeinflusst werden können. Auf Basis der gebildeten statistischen Modelle erfolgte die Vorhersage geeigneter Parameter für eine finale Versuchsreihe. Die abschließenden Demonstratorschweißungen wurden exemplarisch an einer Nickelbasis-Gusslegierung mit besonders hohem Ausscheidungsphasenanteil durchgeführt. Hierfür wurden Schweißungen im I- Stoß an 6,5 mm und 10 mm dicken Blechen ausgeführt. Trotz der mangelnden Schweißeignung und dem hohen Anteil an Ausscheidungsphase des Werkstoffes, zeigten sich nach Optimierung der Prozessparameter keine Heißrisse mehr. T2 - DVS Congress 2020 CY - Online meeting DA - 14.09.2020 KW - Heißrisse KW - Nickelbasis-Superlegierungen KW - Elektonenstrahlschweißen KW - Alloy 247 PY - 2020 SN - 978-3-96144-098-6 VL - 365 SP - 17 EP - 22 PB - DVS Media GmbH CY - Düsseldorf AN - OPUS4-51320 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Raute, J. A1 - Jokisch, T. A1 - Marko, A. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - Influence of electron beam welding parameters on the weld seam geometry of Inconel718 at low feed rates N2 - Ni-based superalloys are well established in various industrial applications, because of their excellentmechanical properties and corrosion resistance at high temperatures. Despite the high development stage anda common industrial use of these alloys, hot cracking remains a major challenge limiting the weldability ofthe materials. As commonly known, the hot cracking susceptibility during welding increases with the amountof precipitation phases. Hence, a large amount of highstrength Ni-Alloys is rated as non-weldable. A newapproach based on electron beam welding at low feed rates shows great potential for reducing the hotcracking tendency of precipitation-hardened alloys. However, geometry and properties of the weld seamdiffer significantly in comparison to the common process range for practical uses. The aim of this study is toinvestigate the influence of welding parameters on the seam geometry at low feed rates between 1 mm/s and10 mm/s. For this purpose, 25 bead on plate welds on a 12 mm thick sheet made of Inconel 718 are carriedout. First, the relevant parameters are identified by performing a screening. Then the effects discovered arefurther studied by using a central composite design. The results show a significant difference between theanalyzed weld seam geometry in comparison to the well-known appearance of electron beam welded seams. KW - Electron beam welding KW - Ni-based superalloy KW - Inconel 718 KW - Low feed rates KW - Seam geometry KW - Hot crack PY - 2020 U6 - https://doi.org/10.3139/120.111614 SN - 0025-5300 VL - 62 IS - 12 SP - 1221 EP - 1227 PB - Carl Hanser Verlag GmbH & Co. KG CY - München AN - OPUS4-52016 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -