TY - CONF A1 - Kromm, Arne T1 - Werkstoffsichere Dickblechschweißung und KI N2 - Der Vortrag zeigt, welche Herausforderungen beim Schweißen sogenannter Dickblechverbindungen bestehen und welche Strategien zur Lösung genutzt werden können. Ansätze zur Fehlerprävention werden vorgestellt. Es wird dargestellt, wie KI-basierte Methoden die Fehlererkennung erleichtern. Letztlich werden Potentiale zur effizienten Fehlerbeseitigung aufgezeigt und der Anwendernutzen herausgestellt. T2 - Mitgliederversammlung der Forschungsvereinigung Stahlanwendung e. V. CY - Essen, Germany DA - 17.12.2025 KW - UP-Schweißen KW - Eigenspannungen KW - Künstliche Intelligenz KW - Reparatur PY - 2025 AN - OPUS4-65210 LA - deu 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 - 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 - 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 - 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 - Yang, Chunliang A1 - Yang, Fan A1 - Meng, Xiangmeng A1 - Putra, Stephen Nugraha A1 - Bachmann, Marcel A1 - Rethmeier, Michael T1 - Phase-field simulation of the dendrite fragmentation by electromagnetic stirring in AA5754 aluminum alloy laser beam welding N2 - A phase-field model including magnetic field induced dendrite fragmentation was established and applied to the cases with different initial crystal nuclear positions for AA5754 aluminum alloy electromagnetic laser beam welding. Compare the calculated results that include dendrite fragmentation caused by the thermal electromagnetic Lorentz force with the results that consider only the thermal electromagnetic Lorentz force, without fragmentation, at the characteristic time instants. Both in the early and late stages, the small fragmentation at the dendrite tip promotes the number of higher-order branches and their growth, especially in the direction perpendicular to the solidification. The later stage fragmentation has the possibility of breaking one grain into several, which verifies the possibility of grain refinement caused by dendrite fragmentation. The fracture surface caused by fragmentation also makes more solid-liquid interfaces and their growth. In addition, the cases with different initial nuclear positions were compared. The grain growth in the low-temperature zone can be inhibited by the equiaxed grains' fragmentation at the high-temperature area (179.8 μm² and 14.7 % start at the center, 115.4 μm² and 9.4 % start at the high-temperature corner, 134.3 μm² and 10.9 % start at the low-temperature corner), which is another kind of grain refinement by the dendrite fragmentation. This kind of inhibition effect on grain growth in the low-temperature region will be enhanced with the increasing time interval between the two crystal nuclei’ appearance (179.8 μm² and 14.7 % when virtual grains appear at t = 4.3803 s and t = 4.3803 s, 134.3 μm² and 10.9 % at t = 4.0977 s and t = 3.9564 s, and 115.4 μm² and 9.4 % at t = 3.8151 s and t = 3.5325 s). KW - Laser beam welding KW - Electromagnetic KW - Aluminum alloys KW - Phase field method KW - Equiaxed grain KW - Dendrite fragmentation PY - 2026 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-656610 DO - https://doi.org/10.1016/j.cwe.2025.100014 SN - 3117-4159 VL - 35 IS - 1 SP - 1 EP - 12 PB - Elsevier B.V. AN - OPUS4-65661 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR 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 model is employed to generate realistic 2D weld pool and keyhole geometries from the welding parameters. We utilize a β-variational autoencoder model with the evidence lower bound 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. KW - Laser beam welding KW - Generative artificial intelligence KW - Machine learning KW - Numerical simulation KW - Weld pool KW - Keyhole dynamics PY - 2026 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-656621 DO - https://doi.org/10.2351/7.0001862 SN - 1042-346X VL - 38 IS - 1 SP - 1 EP - 8 PB - Laser Institute of America AN - OPUS4-65662 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Fabry, Çağtay A1 - Pittner, Andreas A1 - Kaplan, Alexander A1 - Rethmeier, Michael T1 - Digitalisation and Carbon Footprint Evaluation of Gas Metal Arc Welding and Wire-Arc Directed Energy Deposition N2 - This paper showcases how a holistic approach to digitalisation enables data-driven welding applications, exemplarily for a gas metal arc welding (GMAW) laboratory. The workflow integrates advanced process monitoring, synchronised multi-sensor data acquisition and tools for data analytics. A welding domain-specific data exchange format weldx is presented that unifies and aggregates the data sets acquired during process monitoring with final component quality metrics, supporting reuse, traceability, and reproducibility. Two case studies illustrate the approach. First, GMAW parameters are adaptively adjusted according to local seam geometry to compensate joint-preparation deviations from nominal values typical for large-scale steel fabrication. Second, the seamless data aggregation along the welding production chain enables an automatic life-cycle assessment (LCA), quantifying the environmental impacts of additive manufacturing with DED Arc/M and attributing the dominant contributors to the carbon footprint. Collectively, the results indicate that a fully integrated experimental set-up together with standardised data structures and scalable analytics can couple monitoring, control, and sustainability, thereby realising the potential of digitalisation for high-quality and environmentally informed welding production. KW - Digitalisation KW - Arc welding KW - DED-arc KW - Life Cycle Assessment PY - 2026 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-656812 DO - https://doi.org/10.1088/1757-899X/1342/1/012067 SN - 1757-8981 VL - 1342 IS - 1 SP - 1 EP - 15 PB - IOP Publishing AN - OPUS4-65681 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Raute, J. A1 - Schmidt, J. A1 - Bhadeliya, A. A1 - Rehmer, B. A1 - Olbricht, J. A1 - Jokisch, T. A1 - Biegler, M. A1 - Rethmeier, M. T1 - Investigation on seam quality and fatigue crack propagation in electron beam welded additive manufactured Inconel 939 N2 - Laser powder bed fusion of metals (PBF-LB/M) offers great potential for the production of new and spare parts for stationary gas turbines made of nickel superalloys such as Inconel 939 (IN939). In order to enable integration into existing assemblies and overcome design limitations, the additive manufacturing process chain must be expanded by suitable joining techniques. This study compares the electron beam welding of cast IN939 sheets and sheets produced additively using PBF-LB/M. The investigation focuses on the achievable seam quality with regard to geometric irregularities and internal defects in the form of liquation cracks on the microscale in the heat-affected zone. The evaluation of the welded samples shows no differences in the formation of the seam shape between the additively manufactured material and the cast material. For both materials, the highest quality category for beam-welded seams according to DIN EN ISO 13,919–1 was achieved at high welding speeds of 20 mm/s. Regardless of the manufacturing method, both materials show an increase in crack formation with increasing welding speed. However, due to its microstructure, the PBF-LB/M material exhibits significantly fewer microcracks overall. Final crack propagation tests on welded PBF-LB/M samples that were treated using HIP also show stable crack growth without sudden failure, which opens up potential for practical application. KW - Electron beam welding KW - Inconel 939 KW - PBF-LB/M KW - Liquation cracking KW - Fatigue crack propagation PY - 2026 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-657015 DO - https://doi.org/10.1016/j.jajp.2026.100389 SN - 2666-3309 VL - 13 SP - 1 EP - 10 PB - Elsevier B.V. AN - OPUS4-65701 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 - Porosity prediction in laser beam welding with a multimodal physics-informed machine learning framework N2 - Laser beam welding (LBW) of metallic components is a knowledge‑intensive manufacturing process whose quality depends on the complex multi‑physics. However, its engineering application is often hindered by the occurrence of porosity defects. Achieving a thorough understanding and reliable prediction of porosity defects remains difficult because it demands robust representation and reasoning over nonlinear and hard‑to‑observe physical information. In this study, we propose an integrated multimodal physics-informed machine learning (PIML) framework with the help of multi-physical modelling and experimental data to predict the porosity defects in laser beam welding of aluminum alloys. The whole framework contains a multimodal PIML model for predicting the porosity ratio and an ML-based estimator for relevant physical information. By utilizing the scalar welding parameters and high-dimensional physical information (probability of keyhole collapses, cumulative existing time of collapses, and molten pool geometry) as inputs, the multimodal PIML model shows great superiority in predicting the porosity ratio, with a reduction of the mean square error by 45%, compared with the ML model trained only with welding parameters. The ML-based estimator constructed with an encoder‐decoder architecture can accurately reproduce the critical physical information within a timeframe of seconds. By integrating these two ML models, the proposed framework advances engineering informatics by offering a scalable, physics-knowledge‑centric solution for fast and accurate porosity prediction in LBW manufacturing. KW - Laser beam welding KW - Porosity defect KW - Physics-informed machine learning KW - Multimodal model KW - Simulation PY - 2026 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-657589 DO - https://doi.org/10.1016/j.aei.2026.104611 SN - 1474-0346 VL - 74 SP - 1 EP - 12 PB - Elsevier Ltd. AN - OPUS4-65758 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -