TY - RPRT A1 - Bachmann, Marcel A1 - Rethmeier, Michael A1 - Meng, Xiangmeng T1 - Project title: Multi-physical simulation of the influence of an auxiliary magnetic field on the process porosity formation during high-power laser beam welding N2 - In this project, a multiphysically coupled numerical model will be developed to quantitatively describe porosity reduction in high-power laser beam welding of up to 10 mm thick AlMg3 using an oscillating magnetic field. The aim is to gain fundamental insights into the physical dependencies of the introduced electromagnetic forces on the melt pool behavior and the reduction of porosity. With the help of the numerical model, the transient, multi-coupled, three-dimensional problem of heat transfer, liquid flow, free surface deformation, and magnetic induction is to be solved, taking into account temperature-dependent material properties. The numerical modelling of the heat source will integrate all relevant physical mechanisms, for instance, multiple reflections of the laser radiation by an advanced ray tracing model, as well as local Fresnel absorption at the keyhole wall. This allows an analysis of the keyhole fluctuations, which have a dominant influence on the formation of process spores during deep penetration welding, based on physical principles. In addition, further physical factors such as the ablation pressure of the evaporating metal, the Laplace pressure, and Marangoni shear stresses are also to be integrated into the model. To evaluate the pore formation and reduction by means of the electromagnetic forces introduced in the molten pool, suitable models for describing the movement of the pores in the melt are to be developed. For the process pores, their movement can be implemented by tracking their surface under consideration of their internal pressure and temperature. With the help of the simulation model, all key factors for the formation of process pores during laser beam welding of the used aluminum alloy, as well as their avoidance, can be decoupled and analyzed. Accompanying welding tests are planned at BAM on a 20 kW fiber laser and a 16 kW disk laser. The magnetic flux density will be up to 500 mT at a maximum frequency of 5 kHz. The experimental results, in particular temperature measurements, weld cross sections, computer tomography, and X-ray examinations, will be used to verify the multiphysical model and its calibration. Moreover, the models will be validated and quantified by in situ high-speed imaging of the keyhole dynamics in a metal/quartz glass configuration with keyhole illumination by a diode laser coaxial to the processing laser. On the basis of the numerical and experimental results, the dependencies between applied magnetic field, melt pool behavior, and porosity formation will be revealed in this project. KW - Laser beam welding KW - Electromagnetic weld pool control KW - Numerical simulation KW - Process porosity PY - 2026 DO - https://doi.org/10.34657/27669 SP - 1 EP - 14 AN - OPUS4-65335 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bevilacqua, Tommaso A1 - Gumenyuk, Andrey A1 - Habibi, Niloufar A1 - Hartwig, Philipp A1 - Klawonn, Axel A1 - Lanser, Martin A1 - Rethmeier, Michael A1 - Scheunemann, Lisa A1 - Schröeder, Jöerg T1 - Large-scale thermo-mechanical simulation of laser beam welding using high-performance computing: A qualitative reproduction of experimental results N2 - Laser beam welding (LBW) is a non-contact joining technique that has gained significant importance in modern industrial manufacturing. One potential problem, however, is the formation of solidification cracks, which particularly affects alloys with a pronounced melting range. The aim of the present work is the development of computational methods and software tools to numerically simulate LBW. In order to obtain a sufficiently accurate solution, a large number of finite elements has to be used. Therefore, a highly parallel scalable solver framework, based on the software library PETSc, was used to solve this computationally challenging problem on a high-performance computing architecture. Finally, the experimental results and the numerical simulations are compared. They are found to be in good qualitative agreement, which confirms the validity of the numerical simulations and allows for a better interpretation of the experimentally observed strain distribution. KW - Laser beam welding KW - Termo-mechanical processes KW - Solidification cracking KW - High-performance computing KW - Domain decomposition methods PY - 2025 DO - https://doi.org/10.1016/j.rineng.2025.108827 SN - 2590-1230 SP - 1 EP - 33 PB - Elsevier B.V. AN - OPUS4-65290 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Müller, Vinzenz A1 - Klement, Oliver A1 - Sander, Steffen A1 - Biegler, Max A1 - Rethmeier, Michael T1 - Recycling of Stainless Steel Milling Chips for Additive Manufacturing: A Three-Stage Comminution Approach Using a Fine Impact Mill N2 - The production of conventional metal powders for additive manufacturing process is energy intensive and costly. This study introduces a sustainable alternative by recycling stainless steel milling chips as feedstock for laser-powder directed energy deposition. The recycling process employs a three-stage mechanical comminution method utilizing a fine impact mill UPZ100 from Hosokawa Alpine AG. Characterization of the resulting powders is conducted through particle morphology analysis, flowability tests, and mechanical property assessments. The chip-derived powders exhibit comparable aspect ratios and sphericity to conventional water atomized powders, though with reduced flowability due to a pronounced fine fraction content. Elevated levels of oxides are observed, leading to the formation of an oxide layer on specimen blocks, without impairing the mechanical properties. Analyses of porosity, microstructure, and hardness indicate no significant differences when compared to conventional powders from water or gas atomization. This recycling approach not only mitigates waste but also enhances the potential for a circular and sustainable manufacturing process in the additive manufacturing industry and beyond. KW - Directed Energy Deposition KW - Recycling KW - Stainless steel KW - Comminution KW - Powder characteristics PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-652037 DO - https://doi.org/10.1088/1757-899X/1332/1/012014 SN - 1757-8981 VL - 1332 IS - 1 SP - 1 EP - 7 PB - IOP Publishing AN - OPUS4-65203 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Pittner, Andreas A1 - Fabry, Cagtay A1 - Thiele, Marc A1 - Artinov, Antoni A1 - Rethmeier, Michael T1 - Automated Arc Welding and Sensor Based Quality Assessment for Enhancing Fatigue Strength and Structural Reliability of Offshore Wind Turbine Supports N2 - Offshore wind energy plays a crucial role in achieving a carbon dioxide free industrial sector in the coming decades. However, foundation systems for offshore wind turbines significantly impact the overall installation process, posing technical and regulatory challenges. Adopting lightweight design principles in steel construction such as dissolved load bearing structures in jacket foundations offers substantial potential for reducing resource consumption, particularly steel usage. This presentation explores the complete digitalization of welding manufacturing and testing processes, enabling fully automated production and data driven quality assessment of tubular nodes, which are vital components of jacket foundations. The study also investigates the relationship between seam geometry and fatigue strength inspired by bionic design principles. The results demonstrate that tubular nodes can be welded entirely automatically, accommodating geometric tolerances, and that seam geometries ca n be precisely manufactured according to suggestions from e.g. numerical models or other sources . These advancements significantly enhance the fatigue strength and service life of offshore wind turbine support structures. T2 - IIW Annual Assembly 2025 CY - Genova, Italy DA - 23.06.2025 KW - Automated gas metal arc welding KW - Quality assessment and control KW - Support structures KW - Fatigue PY - 2025 AN - OPUS4-65232 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Chaurasia, Prashant Kumar A1 - Cagtay, Fabry A1 - Andreas, Pittner A1 - Rethmeier, Michael T1 - Automated in situ monitoring and analysis of process signatures and build profiles during wire arc directed energy deposition N2 - Wire arc directed energy deposition (DED-Arc) is an emerging metal additive manufacturing process to build near-net shaped metallic parts in a layer-by-layer with minimal material wastage. Automated in situ monitoring and fast-responsive analyses of process signatures and deposit profiles during DED-Arc are in ever demand to print dimensionally consistent parts and reduce post-deposition machining. A comprehensive experimental investigation is presented here involving real-time synchronous measurement of arc current, voltage, and the deposit profile using a novel multi-sensor monitoring framework integrated with the DED-Arc set-up. The recorded current–voltage transients are used to estimate the time-averaged arc power, and energy input in real time for an insight of the influence of wire feed rate and printing travel speed on the deposit characteristics. A unique attempt is made to represent the geometric profiles of the single-track deposits in a generalized mathematical form corresponding to a segmented ellipse, which has exhibited the minimum root-mean-square error of 0.03 mm. The dimensional inconsistency of multi-track deposits is evaluated quantitatively in terms of waviness using build profile monitoring and automated estimation, which is found to increase with an increase in step-over ratio and energy input. For the multi-track mild steel deposits, the suitable range of step-over ratio for the minimum surface waviness is observed to lie between 0.6 and 0.65. Collectively, the proposed framework of synchronized process monitoring and real-time analysis provides a pathway to achieve dimensionally consistent and defect-free parts, and highlights the potential for closed-loop control systems for a wider industrial application of DED-Arc. T2 - IIW Annual Assembly 2025 CY - Genova, Italy DA - 23.06.2025 KW - Additive Manufacturing KW - Arc welding KW - Real-time monitoring and control KW - Dimensional inconsistency KW - DED-arc PY - 2025 AN - OPUS4-65231 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Chaurasia, Prashant Kumar A1 - Fabry, Çağtay A1 - Pittner, Andreas A1 - Rethmeier, Michael T1 - Tandem-GMAW: advanced pulsed and short cicuiting process mode experimental data N2 - This dataset consists of raw recordings for 5 Tandem gas metal arc welding experiments (Tandem-GMAW / T-GMAW). KW - T-GMAW KW - High power welding KW - Deposition efficiency KW - Life Cycle Assessment PY - 2025 DO - https://doi.org/10.5281/zenodo.17951724 PB - Zenodo CY - Geneva AN - OPUS4-65206 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Huo, Wenjie A1 - Schmies, Lennart A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael A1 - Wolter, Katinka T1 - An illumination based backdoor attack against crack detection systems in laser beam welding N2 - Deep neural networks (DNNs) have been wildly used in engineering and have achieved state-of-the-art performance in prediction and measurement tasks. A solidification crack is a serious fault during laser beam welding and it has been proven to be successfully detected using DNNs. Recently, research on the security of DNNs is receiving increasing attention because it is necessary to explore the reliability of DNNs to avoid potential security risks. The backdoor attack is a serious threat, where attackers aim to inject an inconspicuous pattern referred to as trigger into a small portion of training data, resulting in incorrect predictions in the reference phase whenever the input contains the trigger. In this work, we first generate experimental data containing actual cracks in the welding laboratory for training a crack detection model. Then, targeting this scenario, we design a new type of backdoor attack to induce the model to predict the crack as a normal state. Considering the stealthiness of the attack, a common phenomenon during the welding process, illumination, is used as the backdoor trigger. Experimental results demonstrate that the proposed method can successfully attack the crack detection system and achieve over 90% attack success rate on the test set. T2 - 8th ML4CPS 2025 – Machine Learning for Cyber-Physical Systems CY - Berlin, Germany DA - 06.03,2025 KW - System security KW - Welding crack detection KW - Backdoor attack KW - Deep neural networks PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-651357 DO - https://doi.org/10.24405/20021 VL - 2025 SP - 12 EP - 21 PB - Universitätsbibliothek der HSU/UniBw H CY - Hamburg AN - OPUS4-65135 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Müller, Vinzenz A1 - Fasselt, Janek Maria A1 - Klötzer-Freese, Christian A1 - Kruse, Tobias A1 - Kleba-Ehrhardt, Rafael A1 - Biegler, Max A1 - Rethmeier, Michael T1 - Recycling nickel aluminium bronze grinding chips to feedstock for directed energy deposition via impact whirl milling: Investigation on processability, microstructure and mechanical properties N2 - During the production of ship propellers, considerable quantities of grinding chips from nickel aluminium bronze areproduced. This paper examines the mechanical comminution of such chips via impact whirl milling and the utilization of twochip-powder batches as feedstock for a laser-based directed energy deposition process. The materials are characterized viadigital image analysis, standardized flowability tests, scanning electron microscopy and energy dispersive X-ray spectroscopyand are compared to conventional, gas atomized powder. The specimens deposited via directed energy deposition areanalyzed for density, hardness and microstructure and tensile properties for vertical and horizontal build up directions arecompared. At elevated mill rotation speeds, the comminution with impact whirl milling produced rounded particles, favorableflow properties and particle size distribution, making them suitable to deposit additive specimens. The microstructureexhibited characteristic martensitic phases due to the high cooling rates of the additive manufacturing process. The presenceof ceramic inclusions was observed in both the powder and on the tensile fracture surfaces, partly impairing the mechanicalproperties. However, specimens in the vertical build-up direction (Z) showed competitive tensile results, with 775 MPa intensile strength, 455 MPa in yield strength and 12.6 % elongation at break. The findings of this study indicate that recyclingof machining chips to additive manufacturing feedstock can be a viable option for reducing material costs and environmentalimpact. KW - Nickel aluminium bronze KW - Grinding chips KW - Recycling KW - Directed energy deposition KW - Material characterization PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-651214 DO - https://doi.org/10.1016/j.addma.2025.104804 SN - 2214-8604 VL - 105 SP - 1 EP - 9 PB - Elsevier B.V. AN - OPUS4-65121 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Meng, Xiangmeng A1 - Bachmann, Marcel A1 - Kising, Pascal A1 - Yang, Fan A1 - Rethmeier, Michael T1 - Prediction of weld pool and keyhole geometries in high-power laser beam welding through a physics-informed generative artificial intelligence approach N2 - The weld pool and keyhole geometries are critical characteristics in evaluating the stability of the high-power laser beam welding (LBW) process and determining the resultant weld quality. However, obtaining these data through experimental or numerical methods remains challenging due to the difficulties in experimental measurements and the high computational demands of numerical modelling. This paper presents a physics-informed generative approach for predicting weld pool and keyhole geometries in the LBW process. With the help of a well experimentally validated numerical model considering the underlying physics in the LBW, the geometries of the weld pool and keyhole under various welding conditions are calculated, serving as the dataset of the generative model. A Conditional Variational Autoencoder (CVAE) model is employed to generate realistic 2D weld pool and keyhole geometries from the welding parameters. We utilize a β-VAE model with the Evidence Lower Bound (ELBO) loss function and include Kullback-Leibler divergence annealing to better optimize model performance and stability during training. The generated results show a good agreement with the ground truth from the numerical simulation. The proposed approach exhibits the potential of physics-informed generative models for a rapid and accurate prediction of the weld pool geometries across a diverse range of process parameters, offering a computationally efficient alternative to full numerical simulations for process optimization and control in laser beam welding processes. T2 - International Congress of Applications of Lasers & Electro-Optics 2025 CY - Orlando, USA DA - 12.10.2025 KW - Laser beam welding KW - Generative artificial intelligence KW - Machine learning KW - numerical simulation PY - 2025 SP - 1 EP - 10 AN - OPUS4-65075 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bachmann, Marcel A1 - Putra, Stephen Nugraha A1 - Yang, Fan A1 - Meng, Xiangmeng A1 - Pusbatzkies, Pablo A1 - Rethmeier, Michael T1 - Elucidation of the Laser Beam Energy Attenuation by the Vapor Plume Formation during High Power Laser Beam Welding N2 - In high-power laser beam welding, a common phenomenon is the formation of a keyhole caused by the rapid evaporation of the material. Under atmospheric pressure, this evaporation generates a vapor plume that interacts with the laser beam, leading to energy attenuation and scattering of the laser radiation along its path. These interactions affect the stability of the process and the overall weld quality. This study investigates the influence of the vapor plume on the weld pool and keyhole dynamics during high-power laser beam welding of AlMg3 aluminum alloy through experimental and numerical approaches. The primary goal is to identify key vapor plume characteristics, particularly its length fluctuations, and to improve the accuracy of the numerical models. To achieve this, an algorithm was developed for the automated measurement of the vapor plume length using high-speed imaging and advanced data processing techniques. The measured plume length is then used to estimate the additional vapor heating and laser energy attenuation using the Beer–Lambert law. A refined numerical CFD model, incorporating 3D transient heat transfer, fluid flow, and ray tracing, was developed to evaluate the vapor plume’s impact. Results show that already the time-averaged plume length effectively captures its transient influence and aligns well with experimental weld seam geometries. Additionally, energy scattering and absorption caused by the vapor plume led to a wider weld pool at the top surface. The study also shows an increased percentage of keyhole collapses due to the reduced laser power absorption at the keyhole bottom, further highlighting the importance of accurately modeling vapor plume effects. T2 - International Congress of Applications of Lasers & Electro-Optics 2025 CY - Orlando, USA DA - 12.10.2025 KW - Laser beam welding KW - Vapor plume formation KW - Weld pool KW - Keyhole dynamics KW - Numerical modeling PY - 2025 SP - 1 EP - 10 AN - OPUS4-64817 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -