TY - JOUR A1 - Yang, Chunliang A1 - Yang, Fan A1 - Meng, Xiangmeng A1 - Putra, Stephen Nugraha A1 - Bachmann, Marcel A1 - Rethmeier, Michael T1 - Multi-refining effects of an AC electromagnetic field on the microstructure in AA5754 laser beam welding N2 - Fine-grain structure is beneficial to the mechanical properties of the joint. Different kinds of grain and sub-grain structures refined by magnetic field during laser beam welding of AA5754 aluminum alloy are analyzed in this manuscript, and the influences of different magnetic field parameters (magnetic flux density and frequency) in these refining effects are also studied. Using the scanning electron microscope (SEM), the sub-grain structure near the fusion line was obtained, and it was found that the branching promotion and branches refinement can be achieved by the magnetic field. The branches become finer with the magnetic flux density or frequency increases, and this effect is stronger in the dendritic region than in the equiaxed region. The results of electron backscatter diffraction (EBSD) in the equiaxed region show that a high-frequency magnetic field can greatly reduce the average grain size, while a low-frequency one has little effect. A phenomenological nucleation model based on dendrite fragmentation theory was established, and it was introduced into the phase field model to analyze the equiaxed grains evolution process. In addition, another refinement mechanism was also observed. The periodic solidification pattern caused by the unstable solidification in the molten pool can refine the grains, and the magnetic field promotes this kind of grain refinement by promoting the solidification period. KW - Laser beam welding KW - Magnetic field KW - Aluminum alloy KW - Crystal branch development KW - Periodic solidification pattern KW - Grain refinement PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-637222 DO - https://doi.org/10.1016/j.ijheatmasstransfer.2025.127509 SN - 0017-9310 VL - 252 SP - 1 EP - 16 PB - Elsevier Ltd. AN - OPUS4-63722 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Yang, Fan A1 - Meng, Xiangmeng A1 - Putra, Stephen Nugraha A1 - Bachmann, Marcel A1 - Rethmeier, Michael T1 - Numerical studies of process porosity suppression by magnetohydrodynamic technology during the laser beam welding of aluminum alloy N2 - Magnetohydrodynamic (MHD) technology is acknowledged as a promising method for mitigating the porosity defects in laser-welded joints of aluminum alloys. A transient 3D multi-physical numerical model of laser beam welding (LBW), coupled with the MHD and oscillating metal vapor plume model, is developed to study the suppression mechanisms of process porosity by an external magnetic field. The experimental results demonstrate that the porosity ratio is reduced by 93.5 % as the oscillating magnetic field is applied. This significant reduction confirms the effectiveness of the MHD technology in suppressing porosity defects. A downward time-averaged Lorentz force is induced in the weld pool, which affects the fluid flow pattern and the weld pool profile. The change of the flowing pattern in the weld pool by the magnetic field does not always have a positive effect on the porosity suppression. In addition, an analytical model shows that the bubble escape window is expanded by 62 % under the effect of the oscillating magnetic field. The additional upward velocity provided by the electromagnetic expulsive force on the bubbles and the change of weld pool profile are considered favorable factors in eliminating the porosity defects. The numerical and analytical model developed for analyzing the bubble escape window have been validated by experimental results. KW - Laser beam welding KW - Aluminum alloy KW - Process porosity defects KW - Magnetohydrodynamic technology KW - Bubble escape KW - Numerical simulation PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-637642 DO - https://doi.org/10.1016/j.ijheatmasstransfer.2025.127525 SN - 0017-9310 VL - 253 SP - 1 EP - 13 PB - Elsevier Ltd. AN - OPUS4-63764 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Clozel, Melanie A1 - Neumann, Christian A1 - Thore, Johannes A1 - Kolbe, Matthias A1 - Yang, Fan A1 - Gutowski, Olof A1 - Dippel, Ann-Christin A1 - Ruschel, Lucas M. A1 - Busch, Ralf A1 - Altenbach, Christoph A1 - Akuata, Chijioke Kenneth A1 - Zander, Daniela A1 - Wilbig, Janka A1 - Meyer, Andreas T1 - Microstructure formation during gas flow-assisted additive manufacturing of a metallic glass powder on ground and in microgravity N2 - We studied bulk metallic glasses produced from gas flow-assisted laser-based powder bed fusion process, which is capable of additive manufacturing metallic parts in microgravity. A Zr-based bulk metallic glass composition Zr₅₉ˏ₃Cu₂₈ˏ₈Al₁₀ˏ₄Nb₁ˏ₅ has been processed on ground and in microgravity in a compact sounding rocket payload MARS-M. Microstructure characterization was performed using electron microscopy and X-ray diffraction computed tomography, which cope with small amounts of sample materials, especially for those fabricated under microgravity conditions. Very similar microstructures and crystalline fractions are observed in sample manufactured on ground and in microgravity, which shows that process parameters of conventional laser powder bed fusion for manufacturing metallic glasses can be transferred to the processes in microgravity. Two different origins of crystallization have been identified in the Zr₅₉ˏ₃Cu₂₈ˏ₈Al₁₀ˏ₄Nb₁ˏ₅ sample. The preferred occurrence of CuZr₂ at the interlayer boundaries is likely a result of recrystallization from the undercooled melt and hence associated with laser scanning strategy. In contrast, the more uniformly distributed Al₃Zr₄ phase is considered to be triggered by the formation of Cu₂Zr₄O. Thus, for the fabrication of fully amorphous builds both on ground and in space, our findings point to higher scanning speeds and lower oxygen contents, while the latter can also be used to tune the crystalline fractions in the sample. KW - Gas flow-assisted laser-based powder bed fusion KW - Microgravity KW - Glass-forming alloys KW - X-ray diffraction tomography PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-641253 DO - https://doi.org/10.1007/s40964-025-01275-2 SN - 2363-9512 SP - 1 EP - 14 PB - Springer Science and Business Media LLC CY - Cham, Switzerland AN - OPUS4-64125 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Meng, Xiangmeng A1 - Bachmann, Marcel A1 - Yang, Fan A1 - Rethmeier, Michael T1 - Toward prediction and insight of porosity formation in laser welding: A physics-informed deep learning framework N2 - The laser welding process is an important manufacturing technology for metallic materials. However, its application is often hindered by the occurrence of porosity defects. By far, an accurate prediction of the porosity defects and an insight into its formation mechanism are still challenging due to the highly nonlinear physics involved. In this paper, we propose a physics-informed deep learning (PIDL) framework by utilizing mechanistic modeling and experimental data to predict the porosity level during laser beam welding of aluminum alloys. With a proper selection of the physical variables (features) concerning the solidification, liquid metal flow, keyhole stability, and weld pool geometry, the PIDL model shows great superiority in predicting the porosity ratio, with a reduction of mean square error by 41 %, in comparison with the conventional DL model trained with welding parameters. Furthermore, the selected variables are fused into dimensionless features with explicit physical meanings to improve the interpretability and extendibility of the PIDL model. Based on a well-trained PIDL model, the hierarchical importance of the physical variables/procedures on the porosity formation is for the first time revealed with the help of the Shapley Additive Explanations analysis. The keyhole ratio is identified as the most influential factor in the porosity formation, followed by the downward flow-driven drag force, which offers a valuable guideline for process optimization and porosity minimization. KW - Laser beam welding KW - Physics-informed deep learning KW - Porosity prediction KW - Feature fusion KW - Hierarchical importance PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-624708 DO - https://doi.org/10.1016/j.actamat.2025.120740 VL - 286 SP - 1 EP - 13 PB - Elsevier B.V. AN - OPUS4-62470 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 - 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 -