TY - CONF A1 - Bachmann, Marcel T1 - Reconstruction of the time-averaged keyhole geometry in laser beam welding with electromagnetic support N2 - In laser beam welding (LBW), the time-averaged keyhole shape provides statistical insights into the process compared to its transient geometry, offering a deeper understanding of the overall keyhole behaviour. However, capturing the time-averaged keyhole shape through experimental methods remains challenging. In this paper, a reconstruction algorithm for the time-averaged keyhole is developed and integrated into a three-dimensional transient multi-physical coupled numerical model. The algorithm can accurately capture the key characteristics of the keyhole, including its diameter and centroid. In addition, it can also successfully reproduce the experimentally observed phenomena of keyhole tailing. The overall shape of the keyhole appears smooth, without exhibiting obvious instability features. Furthermore, the time-averaged keyhole shape is compared under different magnetic flux densities when an external oscillating magnetic field is applied. The results indicate that the application of external magnetic fields does not fundamentally alter the overall keyhole shape. With increasing magnetic flux density, the trailing tail becomes progressively less pronounced and a noticeable increase in the curvature of the rear wall is observed. The standard deviation of the keyhole diameter can serve as an effective index for evaluating the keyhole instability. Keyhole stability in LBW of aluminium alloys is improved under the assistance of electromagnetic fields, and this stabilization is positively correlated with increasing magnetic flux density. T2 - 14th International Seminar Numerical Analysis of Weldability CY - Seggau, Austria DA - 21.09.2025 KW - Laser beam welding KW - Keyhole reconstruction KW - Electromagnetic weld pool support KW - Porosity defects KW - Keyhole stability PY - 2025 AN - OPUS4-64257 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Fabry, Çağtay T1 - Welding Process Data Management - Perspectives on the BAM Data Store N2 - Arc welding processes are an important manufacturing technology applied to a wide range of critical materials and components such as offshore constructions, pressure vessels and additive manufacturing. Data management for experimental arc welding research faces the challenge of constantly changing experimental setups, incorporating a wide range of custom sensor integrations. Measurements include timeseries process and temperature recordings, 3D-geometry data and video recordings of the process from a sub-millisecond scale to multiple hour-long experiments. In addition, various manual pre-processing steps of the workpieces need to be considered to track the complete manufacturing process and its analysis – from raw materials to final dataset and publication. As a unified RDM system, the BAM Data Store offers the capability to incorporate all steps – albeit not without its own challenges. The talk gives an overview of the different workflows and processing steps along the welding experiments together with their integration into the BAM Data Store. Current solutions and ongoing integration work is explained and discussed. This includes the direct integration and upload of automated processing steps into the Data Store from different machines and sensors using custom Python APIs. Ultimately the complete processing chain across multiple internal steps should be represented in the Data Store. T2 - Data Store Days CY - Berlin, Germany DA - 09.03.2025 KW - Data Store KW - Openbis KW - Research Data Management KW - Reference Data KW - Welding PY - 2025 AN - OPUS4-62976 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bachmann, Marcel 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, FL, USA DA - 12.10.2025 KW - Laser beam welding KW - Vapor plume formation KW - Weld pool KW - Keyhole dynamics KW - Numerical modeling PY - 2025 AN - OPUS4-64816 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Meng, Xiangmeng T1 - Prediction of weld pool and keyhole geometries in high-power laser beam welding through a physics-informed generative 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 - The 45th annual International Congress on Applications of Lasers & Electro-Optics CY - Orlando, FL, USA DA - 12.10.2025 KW - Laser beam welding KW - Generative artificial intelligence KW - Machine Learning KW - Numerical Simulation KW - Weld pool KW - Keyhole dynamics PY - 2025 AN - OPUS4-64812 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Neumann, B. A1 - Gottschalk, G.-F. A1 - Biegler, M. A1 - Goecke, S.-F. A1 - Rethmeier, Michael T1 - Detektion von Bindefehlern mittels neuronaler Netze bei DED-Arc/M von Aluminium anhand von Echtzeit- Schweißstromquellendaten N2 - Mit der Anwendung des Schutzgasschweißens für additive Strukturen wird aufbauend auf Machine-Learning-Modellen, welche bereits zum Überwachen beim Verbindungsschweißen erforscht wurden, ein tiefes neuronales Netz (DNN) zum Monitoring beim DED-Arc/M von Aluminium vorgestellt. Ziel des Machine Learning Modells ist das Identifizieren von Bindefehlern in den aufgebauten Volumina mit prozessbegleitend gemessenen Schweißstromquellensignalen als Input. Es werden durch Algorithmen Merkmalsvariablen in der Vorverarbeitung der Daten extrahiert und die Korrelation zwischen den Merkmalsvariablen und den Bindefehlern analysiert. Durch den vorgestellten Algorithmus werden diese automatisiert als Input an ein DNN übergeben. Diese Arbeit untersucht die Genauigkeit der Klassifizierung von verschiedenen DNN-Architekturen mit jeweils 4 verdeckten Schichten. Als Trainings- und Testsatz werden randomisiert extrahierte Merkmale von defektfreien und fehlerhaften Proben verwendet, wobei Bindefehler zum Teil absichtlich provoziert werden. Das entwickelte neuronale Netz erkennt anhand signifikanter Merkmale aus den Strom- und Spannungsdaten Bindefehler mit einer Genauigkeit von ca. 90%. T2 - 45. Assistentenseminar Füge- und Schweißtechnik CY - Niederaudorf, Germany DA - 09.11.2024 KW - DED-Arc/M KW - Aluminiumschweißen KW - Prozessüberwachung KW - Machine Learning KW - Deep Neural Network KW - Bindefehler PY - 2025 SP - 139 EP - 144 PB - DVS Media GmbH AN - OPUS4-64838 LA - deu 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 - Gumenyuk, Andrey T1 - Strain distribution vs strain evolution during solidification cracking CTW test for laser beam welding of 1 mm austenitic stainless steels N2 - Laser welding is a widely established manufacturing process across many industrial sectors. However, solidification cracking and the weldability of materials have remained contentious issues for many years, particularly concerning the causes of hot crack formation. The local distribution of total strain was measured in close proximity to the solidification zone during laser welding of AISI 304 and AISI 310S stainless steels, using the Controlled Tensile Weldability (CTW) test. In this setup, 1 mm thick weld coupons were subjected to a defined external tensile load during welding. Mechanical loading parameters were varied by adjusting the strain rate and ultimate strain level to identify the critical conditions that lead to solidification crack formation along the weld seam centerline. Using Digital Image Correlation (DIC) and the optical flow method [1], we estimated the local strain distribution at the surface near the molten pool and tracked its evolution across several characteristic zones—before, during, and after the application of mechanical loading. The results revealed that solidification crack formation coincides with regions of high plastic deformation within a critical temperature range. Furthermore, we identified a clear relationship between strain rate and both crack initiation probability and maximum local strain. Importantly, neither strain rate nor maximum strain alone is sufficient to predict cracking; instead, their combined effect must be considered to accurately assess hot cracking susceptibility. T2 - AJP 2025 CY - Coimbra, Portugal DA - 16.10.2025 KW - Laser beam welding KW - Solidification cracking KW - Optical measurement PY - 2025 AN - OPUS4-64431 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Gumenyuk, Andrey T1 - Reducing Noise Impact on Strain Accuracy Measurement by Optical Flow and DIC for Laser Welding Applications N2 - In recent years, non-contact methods for in situ local strain measurement during welding processes have gained increasing importance. This trend is driven by advancements in both measurement technology—such as improved camera systems, illumination sources, and X-ray techniques—and in image processing algorithms for strain evaluation. Laser beam welding poses specific challenges for optical strain measurement due to various types of process-related emissions that impair measurement accuracy. In this study, two different algorithms were applied to analyze the local strain field in the solidification zone during laser welding of AISI 310S stainless steel: the inverse compositional Gauss-Newton algorithm for Digital Image Correlation (DIC) and the Lucas-Kanade method for optical flow analysis [1]. Video sequences were recorded under Controlled Tensile Weldability Test (CTW) conditions, in which the specimens were subjected to a defined external tensile load during welding. This setup consistently induced solidification cracking at the material surface, which could be observed in the video recordings. To enhance the robustness and accuracy of the strain evaluation, various noise reduction techniques were implemented. These included identification and mitigation of erroneous frames caused by process emissions and dynamic disturbances. The resulting strain distributions showed high repeatability across multiple experiments and were in good qualitative agreement with predictions from high-fidelity finite element simulations. [2]. T2 - AJP 2025 CY - Coimbra, Portugal DA - 16.10.2025 KW - Laser beam welding KW - Solidification cracking KW - Optical measurement PY - 2025 AN - OPUS4-64428 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Fabry, Çağtay A1 - Hirthammer, Volker A1 - Scherer, Martin K. T1 - weldx-widgets: advanced visualisation and jupyter widgets for weldx N2 - This package provides advanced visualisation and interactive widgets for the weldx core package. KW - Weldx KW - Welding KW - Research data KW - Visualisation PY - 2025 DO - https://doi.org/10.5281/zenodo.17790485 PB - Zenodo CY - Geneva AN - OPUS4-64973 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Fabry, Çağtay T1 - pytcs: a TwinCAT Scope text export file reader N2 - A Python package for reading exported TwinCAT Scope Files. Export your TwinCAT Scope .svdx files to .txt/.csv and read them into Python. KW - Python KW - TwinCAT Scope KW - File reader KW - File format KW - Measurement data PY - 2025 DO - https://doi.org/10.5281/zenodo.17791125 PB - Zenodo CY - Geneva AN - OPUS4-64975 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -