TY - CONF A1 - Raute, J. A1 - Marquardt, R. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - Untersuchung zum Elektronenstrahlschweißen additiv gefertigter Ni-Basis-Bauteile N2 - Die vorliegende Untersuchung befasst sich mit dem Einfluss des Additive Manufacturing auf die Schweißeignung von Bauteilen aus Inconel 718. Hierfür wurden Proben mittels DED und L-PBF hergestellt und ihr Verhalten in Blindschweißversuchen anhand eines Vergleichs mit konventionellen Gussblechen untersucht. Im zweiten Schritt wurden die verschiedenen additiv hergestellten Proben mit dem Gussmaterial im I-Stoß sowie untereinander verschweißt. Als Schweißverfahren wurde für alle Proben das Elektronenstrahlschweißen angewandt. Zur Auswertung wurde anhand von Schliffen das Nahtprofil vermessen und die Proben auf Poren und Risse untersucht. Zusätzlich wurde die Dichte vermessen und eine Prüfung auf Oberflächenrisse durchgeführt. Das AM-Material zeigte dabei Unterschiede in Nahtform und Defektneigung im Vergleich zum Gusswerkstoff. Insbesondere die DED-proben neigten unter bestimmten Parameterkonstellationen verstärkt zu Porenbildung. Risse konnten nicht beobachtet werden. Trotz auftretender Nahtunregelmäßigkeiten wurde in den kombinierten AM-Schweißproben die Bewertungsgruppe C erreicht. Eine Prüfung der bestehenden Regelwerke zur Schweißnahtbewertung anhand der gewonnenen Erkenntnisse zu additiv gefertigten Proben im Elektronenstrahlschweißprozess zeigte keinen Ergänzungsbedarf. T2 - #additivefertigung: Metall in bestForm CY - Essen, Germany DA - 26.10.2022 KW - Elektronenstrahlschweißen KW - Additive Fertigung KW - Schweißnahtbewertung PY - 2022 SN - 978-3-96144-202-7 VL - 383 SP - 81 EP - 92 PB - DVS-Media GmbH AN - OPUS4-56173 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 - 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 - 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 - CONF A1 - Raute, J. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - Untersuchung zur Herstellung von Cu-Strukturen mittels Wire Electron Beam Additive Manufacturing N2 - Das Additive Manufacturing gewinnt zunehmend an Bedeutung für die Fertigung metallischer Bauteile im industriellen Umfeld. Hierbei wird zunehmend auch auf drahtförmige Ausgangswerkstoffe gesetzt, da diese Vorteile im Handling bieten, bereits in der Industrie etabliert sind und sich in der Regel durch geringere Beschaffungskosten auszeichnen. In den letzten Jahren entwickelte sich neben den bereits im großen Umfeld untersuchten Wire-DED-Verfahren auch eine Prozessvariante unter Nutzung des Elektronenstrahls zur industriellen Marktreife. Dabei zeigt die als Wire Electron Beam Additive Manufacturing bezeichnete Technologie besondere Vorteile gegenüber anderen, zumeist Laser- oder Lichtbogen-basierten DED-Prozessen. Das Verfahren bietet vor allem Potenzial für die Verarbeitung von hochleitfähigen, reflektierenden oder oxidationsgefährdeten Werkstoffen. Insbesondere für die Herstellung von Bauteilen aus Kupferlegierungen zeigt sich der Elektronenstrahl als besonders geeignet. Um das Verfahren einem breiten Anwenderkreis in der Industrie zugänglich zu machen, fehlen jedoch übergreifende Daten zu Leistungsfähigkeit, Prozessgrenzen und Anwendungsmöglichkeiten. Die vorliegende Untersuchung beschäftigt sich mit dieser Problemstellung am Beispiel zweier Cu-Werkstoffe. Dabei werden ein korrosionsbeständiger Werkstoff aus dem maritimen Bereich sowie eine Bronze mit guten Verschleißeigenschaften aus dem Anlagenbau getestet. Über mehrstufige Testschweißungen wurden die physikalisch möglichen Prozessgrenzen ermittelt und Rückschlüsse über die Eignung der Parameter zum additiven Aufbau gezogen. Hierfür wurden zunächst optimale Bereiche für den Energieeintrag anhand von Volumenenergie sowie mögliche Schweißgeschwindigkeiten untersucht. Anschließend wurde die Skalierbarkeit des Prozesses anhand von Strahlstrom und Drahtvorschub getestet. Als wesentliche Zielgrößen wurden dabei Spurgeometrie, Aufmischung und Härte herangezogen. Die Eignung der ermittelten Parameter wurde im letzten Schritt exemplarisch anhand einer additiven Testgeometrie in Form eines Zylinders nachgewiesen. T2 - DVS Congress 2022 Große Schweißtechnische Tagung DVS CAMPUS CY - Koblenz, Germany DA - 19.09.2022 KW - WEBAM KW - Electron beam KW - EBAM KW - Wire electron beam additive manufacturing PY - 2022 SN - 978-3-96144-189-1 VL - 382 SP - 446 EP - 454 PB - DVS Media AN - OPUS4-56058 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 - 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 - Gook, S. A1 - El-Sari, B. A1 - Biegler, M. A1 - Rethmeier, Michael A1 - Lichtenthäler, F. A1 - Stark, M. T1 - Multiple-wire submerged arc welding of high-strength fine-grained steels N2 - Ensuring the required mechanical-technological properties of welds is a critical issue in the application of multi-wire submerged arc welding processes for welding high-strength fine-grained steels. Excessive heat input is one of the main causes for microstructural zones with deteriorated mechanical properties of the welded joint, such as a reduced notched impact strength and a lower structural robustness. A process variant is proposed which reduces the weld volume as well as the heat input by adjusting the welding wire configuration as well as the energetic parameters of the arcs, while retaining the advantages of multiwire submerged arc welding such as high process stability and production speed. KW - Submerged arc welding KW - High-strength fine-grained steels KW - Mechanical properties of the joints KW - Energy parameters of the arc PY - 2022 DO - https://doi.org/10.37434/tpwj2022.01.02 SN - 0957-798X IS - 1 SP - 9 EP - 13 PB - Paton Publishing House CY - Kiev AN - OPUS4-54701 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - El-Sari, B. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - Verbesserung der Vorhersagegüte von künstlichen neuronalen Netzen zum Widerstandspunktschweißen durch Auswertung des dynamischen Widerstands N2 - Das Widerstandspunktschweißen ist ein etabliertes Fügeverfahren in der Automobilindustrie. Es wird vor allem bei der Herstellung sicherheitsrelevanter Bauteile, zum Beispiel der Karosserie, eingesetzt. Daher ist eine kontinuierliche Prozessüberwachung unerlässlich, um die hohen Qualitätsanforderungen zu erfüllen. Künstliche neuronale Netzalgorithmen können zur Auswertung der Prozessparameter und -signale eingesetzt werden, um die individuelle Schweißpunktqualität zu gewährleisten. Die Vorhersagegenauigkeit solcher Algorithmen hängt von dem zur Verfügung gestellten Trainingsdatensatz ab. In diesem Beitrag wird untersucht, inwieweit die Vorhersagegüte eines künstlichen neuronalen Netzes durch Auswertung einer Prozessgröße, dem dynamischen Widerstand, verbessert werden kann. KW - Künstliche Intelligenz KW - Qualität KW - Neuronales Netz KW - Widerstandspunktschweißen PY - 2021 SP - 785 EP - 789 AN - OPUS4-53976 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - El-Sari, B. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - Investigation of the Extrapolation Capability of an Artificial Neural Network Algorithm in Combination with Process Signals in Resistance Spot Welding of Advanced High-Strength Steels N2 - Resistance spot welding is an established joining process for the production of safetyrelevant components in the automotive industry. Therefore, consecutive process monitoring is essential to meet the high quality requirements. Artificial neural networks can be used to evaluate the process parameters and signals, to ensure individual spot weld quality. The predictive accuracy of such algorithms depends on the provided training data set, and the prediction of untrained data is challenging. The aim of this paper was to investigate the extrapolation capability of a multi-layer perceptron model. That means, the predictive performance of the model was tested with data that clearly differed from the training data in terms of material and coating composition. Therefore, three multi-layer perceptron regression models were implemented to predict the nugget diameter from process data. The three models were able to predict the training datasets very well. The models, which were provided with features from the dynamic resistance curve predicted the new dataset better than the model with only process parameters. This study shows the beneficial influence of process signals on the predictive accuracy and robustness of artificial neural network algorithms. Especially, when predicting a data set from outside of the training space. KW - Automotive KW - Artificial intelligence KW - Quality monitoring KW - Resistance spot welding KW - Quality assurance PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-539783 DO - https://doi.org/10.3390/met11111874 VL - 11 IS - 11 SP - 1 EP - 11 PB - MDPI AN - OPUS4-53978 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Raute, J. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - Elektronenstrahl schweißt additiv gefertigte Nickel-Superlegierungen N2 - Die Additive Fertigung ist ideal zur Herstellung und Reparatur komplexer Bauteile aus hochfesten Werkstoffen. Doch es fehlen Fügeverfahren, die Heißrisse vermeiden. Die Lösung heißt Elektronenstrahl. KW - Additive Fertigung PY - 2021 SP - 1 EP - 6 AN - OPUS4-53979 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Brunner-Schwer, Chr. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - Investigation on laser cladding of rail steel without preheating N2 - The contact between train wheels and rail tracks is known to induce material degradation in the form of wear, and rolling contact fatigue in the railhead. Rails with a pearlitic microstructure have proven to provide the best wear resistance under severe wheel-rail interaction in heavy haul applications. High speed laser cladding, a state-of-the-art surface engineering technique, is a promising solution to repair damaged railheads. However, without appropriate preheating or processing strategies, the utilized steel grades lead to martensite formation and cracking during deposition welding. In this study, laser cladding of low-alloy steel at very high speeds was investigated, without preheating the railheads. Process speeds of up to 27 m/min and laser power of 2 kW are used. The clad, heat affected zone and base material are examined for cracks and martensite formation by hardness tests and metallographic inspections. A methodology for process optimization is presented and the specimens are characterized for suitability. Within the resulting narrow HAZ, the hardness could be significantly reduced. T2 - Lasers in Manufacturing Conference 2021 CY - Erlangen, Germany DA - 21.06.2021 KW - High speed laser cladding KW - Preheatin KW - Rail tracks KW - Pearlitic microstructure PY - 2021 AN - OPUS4-53974 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Biegler, M. A1 - El-Sari, B. A1 - Rethmeier, Michael A1 - Finus, F. T1 - Schweißen unter Zug – LME-Eingangsprüfung für die Autoindustrie N2 - Der Trend zum Leichtbau und die Transformation zur E-Mobilität in der Automobilindustrie befeuern die Entwicklung neuer hochfester Stähle für den Karosseriebau. Derartige Werkstoffe sind beim Widerstandspunktschweißen besonders rissanfällig (LME). Das Schweißen unter Zug stellt eine effektive Methode um die LME-Anfälligkeit unterschiedlicher Werkstoffe qualitativ zu bestimmen. KW - Automobilindustrie KW - Widerstandspunktschweißen KW - Liquid Metal Embrittlement KW - Zinkbeschichtung KW - Hochfester Stahl PY - 2021 IS - 6 SP - 54 EP - 55 AN - OPUS4-54057 LA - deu 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 - Raute, J. A1 - Jokisch, T. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - Effects on crack formation of additive manufactured Inconel 939 sheets during electron beam welding N2 - The potential of additive manufacturing for processing precipitation hardened nickel-base superalloys, such as Inconel 939 is considerable, but in order to fully exploit this potential, fusion welding capabilities for additive parts need to be explored. Currently, it is uncertain how the different properties from the additive manufacturing process will affect the weldability of materials susceptible to hot cracking. Therefore, this work investigates the possibility of joining additively manufactured nickel-based superalloys using electron beam welding. In particular, the influence of process parameters on crack formation is investigated. In addition, hardness measurements are performed on cross-sections of the welds. It is shown that cracks at the seam head are enhanced by Welding speed and energy per unit length and correlate with the hardness of the weld metal. Cracking parallel to the weld area shows no clear dependence on the process variables that have been investigated, but is related to the hardness of the heat-affected zone. KW - Electron beam welding KW - Hot Cracks KW - Superalloy KW - Inconel 939 PY - 2021 DO - https://doi.org/10.1016/j.vacuum.2021.110649 SN - 0042-207X VL - 195 SP - 10649 PB - Elsevier Ltd. AN - OPUS4-53689 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Böhne, C. A1 - Meschut, G. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - The Influence of Electrode Indentation Rate on LME Formation during RSW N2 - During resistance spot welding of zinc-coated advanced high-strength steels (AHSSs) for automotive production, liquid metal embrittlement (LME) cracking may occur in the event of a combination of various unfavorable influences. In this study, the interactions of different welding current levels and weld times on the tendency for LME cracking in third-generation AHSSs were investigated. LME manifested itself as high penetration cracks around the circumference of the spot welds for welding currents closely below the expulsion limit. At the same time, the observed tendency for LME cracking showed no direct correlation with the overall heat input of the investigated welding processes. To identify a reliable indicator of the tendency for LME cracking, the local strain rate at the origin of the observed cracks was analyzed over the course of the welding process via finite element simulation. While the local strain rate showed a good correlation with the process-specific LME cracking tendency, it was difficult to interpret due to its discontinuous course. Therefore, based on the experimental measurement of electrode displacement during welding, electrode indentation velocity was proposed as a descriptive indicator for quantifying cracking tendency. KW - Liquid Metal Embrittlement (LME) KW - Crack KW - Resistance Spot Welding (RSW) KW - Advanced High-Strength Steel (AHSS) KW - Welding Current KW - Heat Input KW - Simulation PY - 2022 DO - https://doi.org/10.29391/2022.101.015 SN - 0043-2296 VL - 101 IS - 7 SP - 197-s EP - 207-s PB - American Welding Society CY - New York, NY [u.a.] AN - OPUS4-55600 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Gook, S. A1 - Midik, A. A1 - Biegler, M. A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael T1 - Joining 30 mm Thick Shipbuilding Steel Plates EH36 Using a Process Combination of Hybrid Laser Arc Welding and Submerged Arc Welding N2 - This article presents a cost-effective and reliable method for welding 30 mm thick sheets of shipbuilding steel EH36. The method proposes to perform butt welding in a two-run technique using hybrid laser arc welding (HLAW) and submerged arc welding (SAW). The HLAW is performed as a partial penetration weld with a penetration depth of approximately 25 mm. The SAWis carried out as a second run on the opposite side. With a SAWpenetration depth of 8 mm, the weld cross-section is closed with the reliable intersection of both passes. The advantages of the proposed welding method are: no need for forming of the HLAW root; the SAW pass can effectively eliminate pores in the HLAWroot; the high stability of the welding process regarding the preparation quality of the weld edges. Plasma cut edges can be welded without lack of fusion defects. The weld quality achieved is confirmed by destructive tests. KW - Shipbuilding steel KW - Hybrid laser arc welding KW - Submerged arc welding KW - Hardness KW - Bending test KW - Two-run welding technique KW - Microstructure PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-556090 DO - https://doi.org/10.3390/jmmp6040084 SN - 2504-4494 VL - 6 IS - 4 SP - 1 EP - 11 PB - MDPI CY - Basel AN - OPUS4-55609 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 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 - El-Sari, B. A1 - Biegler, M. A1 - Graf, B. A1 - Rethmeier, Michael T1 - Distortion-based validation of the heat treatment simulation of Directed Energy Deposition additive manufactured parts N2 - Directed energy deposition additive manufactured parts have steep stress gradients and an anisotropic microstructure caused by the rapid thermo-cycles and the layer-upon-layer manufacturing, hence heat treatment can be used to reduce the residual stresses and to restore the microstructure. The numerical simulation is a suitable tool to determine the parameters of the heat treatment process and to reduce the necessary application efforts. The heat treatment simulation calculates the distortion and residual stresses during the process. Validation experiments are necessary to verify the simulation results. This paper presents a 3D coupled thermo-mechanical model of the heat treatment of additive components. A distortion-based validation is conducted to verify the simulation results, using a C-ring shaped specimen geometry. Therefore, the C-ring samples were 3D scanned using a structured light 3D scanner to compare the distortion of the samples with different post-processing histories. KW - Directed Energy Deposition KW - Additive Manufacturing KW - Heat Treatment KW - Numerical Simulation KW - Finite Element Method PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-513153 DO - https://doi.org/10.1016/j.procir.2020.09.146 VL - 94 SP - 362 EP - 366 PB - Elsevier B.V. AN - OPUS4-51315 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - El-Sari, B. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - Investigation of the Application of a C-ring Geometry to validate the Stress Relief Heat Treatment Simulation of Additive Manufactured Austenitic Stainless Steel Parts via Displacement N2 - Directed energy deposition is a metal additive manufacturing process that builds parts by joining material in a layer-by-layer fashion on a substrate. Those parts are exposed to rapid thermo-cycles which cause steep stress gradients and the layer-upon-layer manufacturing fosters an anisotropic microstructure, therefore stress relief heat treatment is necessary. The numerical simulation can be used to find suitable parameters for the heat treatment and to reduce the necessary efforts to perform an effective stress relieving. Suitable validation Experiments are necessary to verify the results of the numerical simulation. In this paper, a 3D coupled thermo-mechanical model is used to simulate the heat treatment of an additive manufactured component to investigate the application of a C-ring geometry for the distortion-based validation of the numerical simulation. Therefore, the C-ring samples were 3D scanned using a structured light 3D scanner to quantify the distortion after each process step. KW - Additive manufacturing KW - Directed energy deposition KW - Laser KW - Heat treatment KW - Numerical simulation PY - 2020 DO - https://doi.org/10.3139/105.110417 VL - 75 IS - 4 SP - 248 EP - 259 PB - Carl Hanser Verlag AN - OPUS4-51318 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Frei, J. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - Resistance spot welding under external load for evaluation of LME susceptibility of zinc coated advanced high strength steel sheets N2 - Some zinc coated advanced high strength steels (AHSS), under certain manufacturing conditions, are known to be prone to liquid metal embrittlement (LME) during resistance spot welding. LME is an undesired phenomenon, which can cause both surface and internal cracks in a spot weld, potentially influencing its strength. An effort is made to understand influencing factors of LME better, and evaluate geometry-material combinations regarding their LME susceptibility. Manufacturers benefit from such knowledge because it improves the processing security of the materials. The experimental procedure of welding under external load is performed with samples of multiple AHSS classes with strengths up to 1200 MPa, including dual phase, complex phase and TRIP steels. This way, externally applied tensile load values are determined, which cause liquid metal embrittlement in the samples to occur. In the future, finite element simulation of this procedure gives access to in-situ stress and strain values present during LME formation. The visualization improves the process understanding, while a quantification of local stresses and strains allows an assessment of specific welded geometries. T2 - ESDAD 2019 CY - Dusseldorf, Germany DA - 24.06.2019 KW - RSW KW - LME KW - Advanced high strength steel KW - Testing method KW - Zinc coated steel PY - 2019 AN - OPUS4-49079 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -