TY - CONF A1 - Pittner, Andreas A1 - Winterkorn, René T1 - Life cycle assessment of fusion welding processes strategies and implementation N2 - In manufacturing, fusion welding processes use a lot of resources, which presents an opportunity to reduce environmental impact. While there is a general understanding of the environmental impact of these processes, it is difficult to quantitatively assess key parameters. This study introduces a welding-specific methodology that uses life cycle assessment (LCA) to evaluate the environmental impact of fusion welding technologies. Our approach analyses the main parameters that affect the environmental performance of different welding techniques, including traditional methods and additive manufacturing through the Direct Energy Deposition-Arc (DED-Arc) process. We integrate real-time resource usage data to offer an innovative framework for directly deriving environmental impacts. This research contributes to optimising welding processes by providing a precise and quantifiable measure of their ecological impact, facilitating the advancement of sustainable manufacturing practices. T2 - CEMIVET - Circular Economy in Metal Industries CY - Berlin, Germany DA - 06.06.2023 KW - Life Cycle Assessment KW - Fusion welding KW - Additive manufacturing KW - DED-Arc PY - 2023 AN - OPUS4-59499 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Marquardt, R. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - Functionally graded material for improved wear resistance manufactured by directed energy deposition N2 - Protecting components against wear and corrosion is a common way to improve their lifetime. This can be achieved by coating them with a hardfacing material. Common coatings consist of materials such as tungsten carbide or cobalt-chromium alloys, also known as Stellite. Hardfacing materials can be deposited by welding methods like plasma welding or laser cladding. The discrete change of the base material to the hardfacing layer can lead to cracks and chipping. Studies showed a reduced risk of cracking when a functionally graded material is used to create a smooth transition between the base and the hardfacing. Gradings from austenitic steel to cobalt-chromium alloys are already known in the literature. However, there is no knowledge about austenitic- ferritic duplex steels as base material. Therefore, this study aims to demonstrate the feasibility of a functionally graded material from duplex steel to cobalt-chromium alloy with a new approach. By using powder-based directed energy deposition, a graded material with smooth material transition is manufactured additively. Cracking and porosity are examined through metallography. Microhardness measurements as well as the analysis of the chemical composition by energy dispersive X-ray spectroscopy and X-ray fluorescence are used to validate the build-up strategy. KW - Additive manufacturing KW - Functionally graded material KW - Functionally graded additive manufacturing KW - Directed energy deposition KW - Laser metal deposition PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-621683 DO - https://doi.org/10.1007/s40964-024-00879-4 SP - 1 EP - 6 PB - Springer Science and Business Media LLC AN - OPUS4-62168 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 - TY - CONF A1 - Gottschalk, Götz-Friedrich A1 - Chaurasia, Prashant Kumar A1 - Goecke, Sven-Frithjof T1 - Zero-defect Printing with DED-GMA via Adaptive Controls N2 - Gas metal arc assisted directed energy deposition (DED-GMA) is a metal additive manufacturing process for fabricating large-scale parts with a higher printing rate. An accurate monitoring and control of the melt pool geometric features is critical for printing zero-defect parts. In this study, the melt pool thermography is used for the real-time detection of the melt pool boundary, centreline, and transient cooling time using an efficient deep learning technique. The presented real-time process monitoring and control methodology using deep learning allows adaptive control of the DED-GMA process. T2 - Twenty-Second International Conference on Flow Dynamics (ICFD 2025) CY - Sendai, Japan DA - 10.11.2025 KW - Additive manufacturing KW - DED-Arc KW - Monitoring KW - Control PY - 2025 SP - 1332 EP - 1335 AN - OPUS4-64837 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Geranmayeh, Ali T1 - Laser Metal Deposition of NiTi Shape Memory Alloys: Influence of Process Parameters on Thermal Profiles and Part Properties N2 - Laser Metal Deposition (LMD), a laser powder–directed energy deposition technology (LP-DED), offers unique flexibility for fabricating complex metallic components. Among candidate materials, Nitinol (NiTi) is particularly attractive due to its shape memory and superelastic properties, though its high sensitivity to processing conditions demands precise parameter control. In this work, prealloyed NiTi powder was deposited as single tracks, and process parameters were optimized using a Design of Experiments methodology. A Central Composite Design (CCD) was implemented with laser power, scan speed, and powder feed rate as inputs, while track’s height, width, aspect ratio, and dilution served as optimization responses. To address the strong susceptibility of NiTi to heat accumulation, hatch spacing was further optimized using a geometrically derived formula, enabling the use of maximum spacing while ensuring dense parts with smooth surfaces and minimal waviness. The presented framework establishes a systematic route for parameter optimization in NiTi LMD, offering practical guidelines for balancing densification and surface quality. T2 - WGF Assistant Seminar CY - Rechenberg-Bienenmühle, Germany DA - 10.09.2025 KW - Additive manufacturing KW - Shape memory alloys KW - Nitinol KW - Laser metal deposition KW - Design of experiments PY - 2025 AN - OPUS4-64164 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 - JOUR A1 - Dak, Gaurav A1 - Barik, Birendra Kumar A1 - Chaurasia, Prashant Kumar A1 - Gottschalk, Götz-Friedrich A1 - Shahane, Akshay Manoj A1 - Goecke, Sven-F. A1 - De, Amitava T1 - Probing dimensional consistency of deposited structures during out-of-position wire arc directed energy deposition N2 - Wire arc directed energy deposition (DED-Arc) using a gas metal arc (GMA) welding power source is cited as DED-GMA that fabricates a part by layer-by-layer deposition of molten wire droplets along horizontal and out-of-position inclined trajectories. For the out-of-position trajectories, a smooth deposition of material is impaired by the gravitational force on the molten wire droplets, resulting in uneven and inconsistent deposit profiles. We present here a detailed experimental investigation to realize the effect of the out-of-position inclinations on the quality of the deposited structure for DED-GMA with a steel and an aluminium filler wire. The evolution of the droplet transfer and melt pool during the out-of-position inclined deposition is probed through high-speed videography at different baseplate angles and commonly used scanning strategies. An analytical model is proposed further based on force equilibrium analysis for a prior estimation of the out-of-position inclined deposit profile, which can help design dimensionally consistent and structurally sound parts using DED-GMA. KW - DED-Arc KW - Additive manufacturing KW - Out-of-position deposition KW - High-speed videography KW - Force PY - 2025 DO - https://doi.org/10.1007/s40194-025-02291-0 SN - 0043-2288 SP - 1 EP - 20 PB - Springer Science and Business Media LLC AN - OPUS4-65350 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Biegler, M. A1 - Wang, J. A1 - Kaiser, L. A1 - Rethmeier, Michael T1 - Automated Tool-Path Generation for Rapid Manufacturing of Additive Manufacturing Directed Energy Deposition Geometries N2 - In additive manufacturing (AM) directed energy deposition (DED), parts are built by welding layers of powder or wire feedstock onto a substrate with applications for steel powders in the fields of forging tools, spare parts, and structural components for various industries. For large and bulky parts, the choice of toolpaths influences the build rate, the mechanical 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 DED processes. This contribution presents automated tool-path generation approaches and discusses the results for arbitrary geometries. Socalled “zig-zag” and “contour-parallel” processing strategies are investigated and the tool-paths are automatically formatted into machine-readable g-code for experimental validation to build sample geometries. The results are discussed in regard to volume-fill, microstructure, and porosity in dependence of the path planning according to photographs and metallographic cross-sections. KW - Porosity KW - Path planning KW - Mechanical properties KW - Laser metal deposition KW - Additive manufacturing PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-510454 DO - https://doi.org/10.1002/srin.202000017 VL - 91 IS - 11 SP - 2000017 PB - WILEY-VCH Verlag GmbH & co. KGaA CY - Weinheim AN - OPUS4-51045 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 DO - 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 -