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 - 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 - CONF A1 - Marquardt, R. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - Funktional gradierte Materialien auf Basis von Stellite und Stahl im Laserpulver-Auftragschweißen N2 - Das Hinzufügen von Stellite auf Stahl ist eine typische Vorgehensweise um Bauteile gegen Verschleiß und Korrosion zu schützen. Der Sprung in den Materialeigenschaften kann jedoch zu Rissen und somit zum Versagen der Beschichtung führen. Um die Lebensdauer von Beschichtungen zu erhöhen wird daher ein gradierter Übergang mit verschiedenen Materialpaarungen untersucht. T2 - 13. Fachtagung Verschleiss- und Korrosionsschutz von Bauteilen durch Auftragschweißen CY - Haale (Saale), Germany DA - 22.06.2022 KW - FGM KW - DED KW - AM KW - Functionally Graded Materials KW - Additive Manufacturing KW - Directed Energy Deposition PY - 2022 SP - 66 EP - 73 AN - OPUS4-55504 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 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 - JOUR A1 - Biegler, M. A1 - Elsner, B. A1 - Graf, B. A1 - Rethmeier, Michael T1 - Geometric distortion-compensation via transient numerical simulation for directed energy deposition additive manufacturing N2 - Components distort during directed energy deposition (DED) additive manufacturing (AM) due to the repeated localised heating. Changing the geometry in such a way that distortion causes it to assume the desired shape – a technique called distortion-compensation – is a promising method to reach geometrically accurate parts. Transient numerical simulation can be used to generate the compensated geometries and severely reduce the amount of necessary experimental trials. This publication demonstrates the simulation-based generation of a distortioncompensated DED build for an industrial-scale component. A transient thermo-mechanical approach is extended for large parts and the accuracy is demonstrated against 3d-scans. The calculated distortions are inverted to derive the compensated geometry and the distortions after a single compensation iteration are reduced by over 65%. KW - DED KW - Welding simulation KW - Dimensional accuracy KW - Additive manufacturing KW - Laser metal deposition KW - LMD PY - 2020 U6 - https://doi.org/10.1080/13621718.2020.1743927 SP - 1 EP - 8 PB - Taylor & Francis AN - OPUS4-50877 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Biegler, M. A1 - Wang, J. A1 - Graf, B. A1 - Rethmeier, Michael T1 - Automated tool-path generation for rapid manufacturing and numerical simulation of additive manufacturing LMD geometries N2 - In additive manufacturing (AM) Laser Metal Deposition (LMD), parts are built by welding layers of powder feedstock onto a substrate. Applications for steel powders include forging tools and structural components for various industries. For large parts, the choice of tool-paths influences the build-rate, the part performance and the distortions in a highly geometry-dependent manner. With weld-path lengths in the range of hundreds of meters, a reliable, automated tool path generation is essential for the usability of LMD processes. In this contribution, automated tool-path generation approaches are shown and their results are discussed for arbitrary geometries. The investigated path strategies are the classical approaches: “Zig-zag-” and “contour-parallel-strategies”. After generation, the tool-paths are automatically formatted into g-code for experimental build-up and ASCII for a numerical simulation model. Finally, the tool paths are discussed in regards to volume-fill, microstructure and porosity for the experimental samples. This work presents a part of the IGF project 18737N “Welding distortion simulation” (FOSTA P1140) T2 - 4th European Steel Technology and Application Days CY - Dusseldorf, Germany DA - 24.06.2019 KW - Additive manufacturing KW - Directed Energy Deposition KW - Path planning KW - DED KW - Mechanical properties KW - Porosity PY - 2019 SP - 1 AN - OPUS4-50045 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - El-Sari, B. A1 - Biegler, M. A1 - Graf, B. A1 - Rethmeier, Michael T1 - Numerische Simulation einer AM-Prozesskette im DED Auftragschweißen N2 - Das DED Auftragschweißen ist ein additives Fertigungsverfahren für Metalle, bei dem das Material schichtweise auf ein Substrat aufgetragen wird. Die schnellen Temperaturzyklen rufen Spannungsgradienten im Bauteil hervor. Der schichtweise Aufbau der Bauteile verursacht eine anisotrope Mikrostruktur. Mittels nachgelagerter Wärmebehandlung können diese Effekte verringert werden. Im anschließenden Schritt der Prozesskette wird das additiv hergestellte Bauteil mittels Drahterodieren von dem Substrat abgetrennt. In diesem Beitrag wird eine thermo-mechanische Simulation der gesamten Prozesskette vorgestellt, welche den additiven Aufbau, Wärmebehandlung und das Abtrennen vom Substrat beinhaltet. Anstelle der in der Literatur üblichen schichtweisen Modellierungsstrategie für die DED Simulation wird das gesamte Bauteil in einem Stück vernetzt und der vollständig transiente, schichtweise Materialauftrag über Elementgruppen realisiert. Im Gegensatz zu früheren Simulationen muss der nichtlineare Kontakt zwischen den Schichten nicht berücksichtigt werden, was die Rechenzeiten deutlich verkürzt. Das Modell wurde validiert mittels Abgleiches des Verzugs aus Simulation und Experiment. Die Proben, bestehend aus DIN 1.4404 (AISI 316L), wurden nach jedem Prozessschritt 3D gescannt um den Verzug zu quantifizieren. Zusätzlich wurden Querschnitte und Härtetests nach Vickers von unterschiedlich behandelten Proben durchgeführt, um den Effekt der Wärmebehandlung auf die Mikrostruktur und die Härte des Bauteils zu untersuchen. T2 - 20. Roundtable Simulating Manufacturing CY - Marburg, Germany DA - 22.05.2019 KW - Numerische ISmulation KW - DED KW - AM KW - Laser-Pulver-Auftragschweißen PY - 2019 SP - 1 EP - 14 AN - OPUS4-50046 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Biegler, M. A1 - Graf, B. A1 - Rethmeier, Michael T1 - Assessing the predictive capability of numerical additive manufacturing simulations via in-situ distortion measurements on a LMD component during build-up N2 - Due to rapid, localized heating and cooling, distortions accumulate in additive manufactured laser metal deposition (LMD) components, leading to a loss of dimensional accuracy or even cracking. Numerical welding simulations allow the prediction of these deviations and their optimization before conducting experiments. To assess the viability of the simulation tool for the use in a predictive manner, comprehensive systems as well as to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to analyze a product or one product family on the physical level. Different product families, however, may differ largely in terms of the number and nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to Cluster these products in new assembly oriented product families for the optimization. KW - Laser metal deposition KW - Directed Energy Deposition KW - DED KW - Welding Simulation KW - Digital Image Correlation KW - Cimensional Accuracy PY - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-502567 VL - 74 SP - 158 EP - 162 PB - Elsevier AN - OPUS4-50256 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Biegler, P. ED - Marko, A. ED - Graf, B. ED - Rethmeier, Michael T1 - Finite element analysis of in-situ distortion and bulging for an arbitrarily curved additive manufacturing directed energy deposition geometry N2 - With the recent rise in the demand for additive manufacturing (AM), the need for reliable simulation tools to support experimental efforts grows steadily. Computational welding mechanics approaches can simulate the AM processes but are generally not validated for AM-specific effects originating from multiple heating and cooling cycles. To increase confidence in the outcomes and to use numerical simulation reliably, the result quality Needs to be validated against experiments for in-situ and post-process cases. In this article, a validation is demonstrated for a structural thermomechanical simulation model on an arbitrarily curved Directed Energy Deposition (DED)part: at first, the validity of the heat input is ensured and subsequently, the model’s predictive quality for in-situ deformation and the bulging behaviour is investigated. For the in-situ deformations, 3D-Digital Image Correlation measurements are conducted that quantify periodic expansion and shrinkage as they occur. The results show a strong dependency of the local stiffness of the surrounding geometry. The numerical Simulation model is set up in accordance with the experiment and can reproduce the measured 3-dimensional in-situ displacements. Furthermore, the deformations due to removal from the substrate are quantified via 3D-scanning, exhibiting considerable distortions due to stress relaxation. Finally, the prediction of the deformed shape is discussed in regards to bulging simulation: to improve the accuracy of the calculated final shape, a novel Extension of the model relying on the modified stiffness of inactive upper layers is proposed and the experimentally observed bulging could be reproduced in the finite element model. KW - DED KW - Welding simulation KW - Additive manufacturing KW - Dimensional accuracy KW - Digital image correlation PY - 2018 U6 - https://doi.org/10.1016/j.addma.2018.10.006 SN - 2214-8604 SN - 2214-7810 VL - 24 SP - 264 EP - 272 PB - Elsevier AN - OPUS4-47226 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Marquardt, R. A1 - Bähring, S. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - Künstliche Neuronale Netze zur Qualitätsprognose von Funktional Gradierten Materialien im laserbasierten Directed Energy Deposition N2 - Durch pulverbasiertes Directed-Energy Deposition lassen sich Gradierungen fertigen, um diskrete Materialübergänge zu vermeiden und die Lebensdauer von Hartschichten zu erhöhen. Die Kombination aus Stahl als Basiswerkstoff und einer verschleiß- und korrosionsbeständigen Co-Cr Legierung verspricht durch Vermeiden von Spannungskonzentrationen das Verhindern von Abplatzungen und Rissen in der Schutzschicht. Um die Qualität des gefertigten Bauteils zu beurteilen, liegen für solche Funktional Gradierten Materialien (FGM) wenig Erkenntnisse vor. Daher wird im Rahmen dieser Studie eine Methodik erarbeitet, um die relative Dichte eines Funktional Gradierten Materials auf Stahl und Co-Cr Basis mittels Maschinendaten zu bestimmen. Anschließend wird unter Einsatz eines künstlichen neuronalen Netzes anhand von Sensordaten die relative Dichte vorhergesagt. Das trainierte Netz erreicht eine Vorhersagegenauigkeiten von 99,83%. Abschließend wird eine Anwendung anhand von einem Demonstrator gezeigt. T2 - 3. Fachtagung Additive Manufacturing CY - Halle, Germany DA - 05.10.2023 KW - Directed Enery Deposition KW - Künstliche Neuronale Netze KW - Additive Manufacturing KW - DED KW - KI KW - AM PY - 2023 SP - 1 EP - 8 PB - SLV Halle AN - OPUS4-58692 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -