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 - CONF A1 - Yang, Fan A1 - Meng, Xiangmeng A1 - Putra, Stephen Nugraha A1 - Bachmann, Marcel A1 - Rethmeier, Michael T1 - Assessment of keyhole stability in laser beam welding with external magnetic field using numerical simulation N2 - The challenge of understanding the physical mechanisms behind porosity reduction by a magnetic field during laser beam welding (LBW) is partly due to the difficulty in quantitatively evaluating keyhole stability. The commonly used index, such as keyhole depth, is typically one-dimensional, which is insufficient to capture the dynamic and three-dimensional fluctuations of the keyhole. In this paper, by utilizing a 3D multiphysical model of LBW with magnetic field, a novel keyhole geometry reconstruction algorithm has been developed to describe the keyhole profile and its fluctuation in a statistical manner to evaluate keyhole stability quantitatively. An equivalent diameter is proposed in this algorithm to reduce the irregularity of the keyhole geometry. The calculation results indicate that the time-averaged keyhole shape over 300 ms in the LBW of steel is conical, regardless of the application of an external magnetic field, which provides a more representative shape. Meanwhile, it is observed from the statistical aspect that the keyhole diameter becomes smaller, except the top part, under the influence of the magnetic field. The standard deviation of the equivalent diameter can be used as a physical variable to assess the keyhole stability quantitatively. The application of an external magnetic field can produce a noticeable reduction of the standard deviation of the equivalent diameter, namely, stabilizing the keyhole during LBW of steel. However, the different contribution from the keyhole stability affected by a magnetic field in suppressing porosity is different with materials. T2 - International Congress of Applications of Lasers & Electro-Optics 2024 CY - Hollywood, Los Angeles, USA DA - 04.11.2024 KW - Deep penetration laser beam welding KW - Numerical simulation KW - Keyhole shape KW - Keyhole stability KW - Magnetic field PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-616024 DO - https://doi.org/10.2351/7.0001528 VL - 36 IS - 4 SP - 1 EP - 7 PB - AIP Publishing AN - OPUS4-61602 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Yang, Keke A1 - Wang, Zhuoqun A1 - Haak, Viktor A1 - Olfert, Viktoria A1 - El-Sari, Bassel A1 - Hein, David A1 - Biegler, Max A1 - Rethmeier, Michael A1 - Meschut, Gerson T1 - A novel welding schedule for expanding the expulsion-free process window in resistance spot welding of dissimilar joints with ultra-high strength steel N2 - This study introduces a novel approach to expanding the maximum expulsion-free process window in resistance spot welding (RSW) of dissimilar joints between ultra-high strength steel (UHSS) and mild steel. Quantitative analysis revealed that expulsion is driven by the interaction between nugget growth rate and plastic shell thickness. Based on this finding, the welding schedule was optimized by applying a preheating current to form an initial plastic shell, followed by a ramp-up current profile during the main welding phase. Welding simulations indicated that the ramp-up current slowed nugget growth, improved plastic shell formation, and prevented nugget breakthrough, thereby reducing the risk of expulsion. Experimental validation showed a 19 % increase in maximum expulsion-free heat input, with the nugget diameter increasing by 7.6 % to 8.94 mm compared to the reference welding schedule. Furthermore, even when expulsion occurred beyond the process window, this optimization delayed its occurrence, minimizing its impact on spot weld quality. Finally, the optimized welding schedule also exhibited significant robustness. Despite a 2 mm initial gap disturbance, the maximum expulsionfree heat input increased by 57 %, while the nugget diameter grew by 30 % to 8.92 mm. These results confirm that the proposed approach effectively extends the process window by preventing expulsion and enhances process stability. KW - Expulsion KW - Resistance spot welding KW - Finite element modelling KW - Preheating KW - Ultra-high-strength steel PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-626877 DO - https://doi.org/10.1016/j.jmapro.2025.02.009 SN - 2212-4616 VL - 137 SP - 306 EP - 309 PB - Elsevier BV AN - OPUS4-62687 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Artinov, Antoni A1 - Meng, Xiangmeng A1 - Bachmann, Marcel A1 - Rethmeier, Michael T1 - Study on the transition behavior of the bulging effect during deep penetration laser beam welding N2 - The present work is devoted to the study of the transition behavior of the recently confirmed widening of the weld pool, known as the bulging effect, during high-power deep penetration laser beam welding of thick unalloyed steel sheets. A three-dimensional transient multi-physics numerical model is developed, allowing for the prediction of the bulge formation and the study of its temporal behavior. The model is generalized to account automatically for the transition from partial to complete penetration. Several experimental measurements and observations, such as drilling period, weld pool length, temperature, efficiency, and metallographic cross-sections are used to verify the model and assure the plausibility of the numerical results. The analysis of the calculated temperature and velocity distributions, as well as the evolution of the keyhole geometry, shows that the formation of a bulging region strongly depends on the penetration depth of the weld. Based on the numerical results, the bulge is found to occur transiently, having its transition from a slight bulge to a fully developed bulging between penetration depths of 6 mm and 9 mm, respectively. KW - Laser beam welding KW - Deep penetration KW - Bulge formation KW - Numerical modeling PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-545067 DO - https://doi.org/10.1016/j.ijheatmasstransfer.2021.122171 VL - 184 SP - 122171 PB - Elsevier Ltd. AN - OPUS4-54506 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 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 - Bachmann, Marcel A1 - Meng, Xiangmeng A1 - Artinov, Antoni A1 - Rethmeier, Michael T1 - Elucidation of the Bulging Effect by an Improved Ray-Tracing Algorithm in Deep Penetration Wire Feed Laser Beam Welding and its Influence on the Mixing Behavior N2 - Herein, an improved ray-tracing routine using a virtual mesh refinement approach is adopted in a 3D transientmultiphysics computational fluid Dynamics model for deep penetration wire feed laser beam welding. In a previous study, it was shown that the improved localization of the reflection points of the subrays within the keyhole leads to a more realistic development of the keyhole Depth being validated with experimental results. Another effect investigated in Detail herein is a drastic change in the flow behavior in the weld pool, which promotes the occurrence of a necking area in the solidification line and subsequent bulging under specific circumstances. This has a detrimental effect on the filler material element transport in the weld pool, leading to an inhomogeneous dilution of the added material. The numerical observations are backed up by experimentally obtained data, allowing to provide a clear physics-based explanation of the reduced mixing behavior of the filler wire in the melt pool. KW - Bulging effects KW - Numerical welding simulations KW - Ray-tracing methods KW - Wire feed laser beam welding PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-543322 DO - https://doi.org/10.1002/adem.202101299 SP - 1 EP - 9 PB - Wiley AN - OPUS4-54332 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 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 - Straße, Anne A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael T1 - Study on Duplex Stainless Steel Powder Compositions for the Coating of thick Plates of Laser Beam Welding N2 - Duplex stainless steels combine the positive properties of its two phases, austenite and ferrite. Due to its good corrosion resistance, high tensile strength, and good ductility, it has multiple applications. But laser beam welding of duplex steels changes the balanced phase distribution in favor of ferrite. This results in a higher vulnerability to corrosion and a lower ductility. Herein, different powder combinations consisting of duplex and nickel for coating layers by laser metal deposition (LMD) are investigated. Afterward, laser tracks are welded, and the temperature cycles are measured. The ferrite content of the tracks is analyzed by feritscope, metallographic analysis, and electron backscatter diffraction. The goal is the development of a powder mixture allowing for a duplex microstructure in a two-step process, where first the edges of the weld partners are coated with the powder mixture by LMD and second those edges are laser beam welded. The powder mixture identified by the pretests is tested in the two-step process and analyzed by metallographic analysis, energy-dispersive X-ray spectroscopy, and Vickers hardness tests. The resulting weld seams show a balanced duplex microstructure with a homogenous nickel distribution and a hardness of the weld seam similar to the base material. KW - Duplex AISI 2205 KW - Laser metal deposition KW - Laser beam welding KW - Nickel KW - Stainless steels PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-547024 DO - https://doi.org/10.1002/adem.202101327 SN - 1438-1656 SP - 1 EP - 11 PB - Wiley-VCH CY - Weinheim AN - OPUS4-54702 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Geisen, O. A1 - Müller, V. A1 - Graf, B. A1 - Rethmeier, Michael T1 - Integrated weld preparation designs for the joining of L‑PBF and conventional components via TIG welding N2 - size limitations and high production costs of L-PBF make it competitive for smaller, highly complex components, while the less complex elements of an assembly are manufactured conventionally. This leads to scenarios that use L-PBF only where it’s beneficial, and it require an integration and joining to form the final product. For example, L-PBF combustion swirlers are welded onto cast parts to produce combustion systems for stationary gas turbines. Today, the welding process requires complex welding fixtures and tack welds to ensure the correct alignment and positioning of the parts for repeatable weld results. In this paper, L-PBF and milled weld preparations are presented as a way to simplify the Tungsten inert gas (TIG) welding of rotationally symmetrical geometries using integrated features for alignment and fixation. Pipe specimens with the proposed designs are manufactured in Inconel 625 using L-PBF and milling. The pipe assembly is tested and TIG welding is performed for validation. 3D scans of the pipes before and after welding are evaluated, and the weld quality is examined via metallography and computed tomography (CT) scans. All welds produced in this study passed the highest evaluation group B according to DIN 5817. Thanks to good component alignment, safe handling, and a stable welding process, the developed designs eliminate the need for part-specific fixtures, simplify the process chain, and increase the process reliability. The results are applicable to a wide range of components with similar requirements. KW - L-PBF KW - AM feature integration KW - TIG welding KW - Integrated alignment features KW - Pipe weld preparation KW - Dissimilar joints KW - Inconel 625 PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-547042 DO - https://doi.org/10.1007/s40964-021-00221-2 SN - 2363-9512 SP - 1 EP - 11 PB - Springer AN - OPUS4-54704 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 - 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 -