TY - JOUR A1 - Akyel, Fatma A1 - Üstündag, Ömer A1 - Bakir, Nasim A1 - Brunner-Schwer, Christian A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael T1 - Influence of heat input on cooling rates and mechanical properties of laser hybrid welded thick structural steels N2 - Laser hybrid welding presents several challenges when used to weld thick steels. A typical weld is divided into the arcdominated and laser-dominated zone. These zones lead to variations in the mechanical properties of the weld. The laserdominated zone is of particular importance regarding mechanical properties, notably Charpy impact toughness, due to the high cooling rates and the absence of filler wire. The low heat input of the laser can lead to martensitic microstructure causing hardening and deterioration of impact toughness. The high heat input of the arc can lead to grain coarsening and even loss of impact toughness. This study examines the influence of heat input on the cooling rates, microstructure and mechanical properties of single-pass laser hybrid welded steels of S355J2 and EH36 with thicknesses up to 30 mm. The experiments were performed with a 20-kW fibre laser and a contactless electromagnetic weld backing in the butt-joint configuration in 1G welding position. The cooling time was measured in three different locations near the fusion lines corresponding to different heights of the seam using a special configuration with pyrometers, collimators and optical fibres. The test specimens for the Charpy impact testing and tensile testing were extracted in three different depths. The experiments indicated that a heat input of 1.6 kJ/mm–2 kJ/mm, 2 kJ/mm–2.4 kJ/mm and 3.7 kJ/mm were recommended when single-pass laser hybrid welding of 20-, 25-, and 30-mm-thick structural steels regarding the minimum requirements of the mechanical properties, respectively. KW - Laser hybrid welding KW - Heat input KW - Mechanical properties KW - Charpy impact toughness KW - Cooling rate PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-625282 DO - https://doi.org/10.1007/s40194-025-01924-8 SN - 1878-6669 SP - 1 EP - 15 PB - Springer CY - United Kingdom AN - OPUS4-62528 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Savitsky, Viktor A1 - Schmies, Lennart A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael T1 - Comparative performance of DIC and optical flow algorithms for displacement and strain analysis in laser beam welding N2 - The measurement of strain and displacement in the context of the welding process represents a significant challenge. Optical methods, such as digital image correlation (DIC) or optical flow algorithms, have demonstrated their efficacy in robust and reliable data acquisition in harsh environments, including those encountered in welding processes. Concurrently, a trade-off between the accuracy of the measurement and the computational resources required for the associated calculations must be evaluated on a case-by-case basis. The application of filters to initial images represents a technique that serves to enhance the quality and accuracy of the strain and displacement prediction. In the present study, the estimated error of two algorithms, namely the Lucas-Kanade (LK) and the inverse compositional Gauss-Newton (IC-GN), is compared on the basis of both synthetic and real welding images. The displacement field is evaluated for different zones in the laser weld seam with varying contrast performance. Based on the aforementioned results, a strain calculation was conducted for both methods, which yielded comparable results for the LK and IC-GN algorithms. KW - Laser speckle KW - DIC KW - Optical flow KW - Error estimation KW - Strain measurement KW - Laser beam welding PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-625919 DO - https://doi.org/10.1016/j.optlaseng.2025.108870 SN - 1873-0302 VL - 187 SP - 1 EP - 15 PB - Elsevier Ltd. AN - OPUS4-62591 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Huo, Wenjie A1 - Schmies, Lennart A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael A1 - Wolter, Katinka T1 - Prediction of mean strain from laser beam welding images and detection of defects via strain curves based on machine learning N2 - With the advancement of machine learning, many predictions and measurements in visual tasks can be achieved by convolutional neural networks (CNNs). Solidification hot cracking is a significant defect in laser beam welding, commonly encountered in practical applications. Existing theories indicate that the formation of cracks is closely related to strain accumulation near the solidification front. In this paper, we first leverage supervised Regression networks to design CNNs that achieve real-time average strain estimation for each frame in the collected welding videos. Two different architectures are proposed and compared: the first model stacks two frames at a set interval and feeds them into the network, while the second model extracts image features individually and predicts the results by calculating the correlation between them. Each network has its own advantages in Terms of computational efficiency and accuracy. Finally, we further train a multilayer perceptron (MLP) classification model that can detect the occurrence of cracks based on the predicted strain behaviors. KW - Laser beam welding KW - Mean strain prediction KW - Solidification cracking detection Convolutional neural networks KW - Convolutional neural networks PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-644495 DO - https://doi.org/10.1016/j.optlastec.2025.113975 SN - 0030-3992 VL - 192, Part F SP - 1 EP - 8 PB - Elsevier Ltd. AN - OPUS4-64449 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Hartwig, Philipp A1 - Bakir, Nasim A1 - Gumenyuk, Andrey A1 - Scheunemann, Lisa A1 - Schröder, Jörg A1 - Rethmeier, Michael T1 - A Physically Motivated Heat Source Model for Laser Beam Welding N2 - In this contribution, we present a physically motivated heat source model for the numerical modeling of laser beam welding processes. Since the calibration of existing heat source models, such as the conic or Goldak model, is difficult, the representation of the heat source using so-called Lamé curves has been established, relying on prior Computational Fluid Dynamics (CFD) simulations. Lamé curves, which describe the melting isotherm, are used in a subsequent finite-element (FE) simulation to define a moving Dirichlet boundary condition, which prescribes a constant temperature in the melt pool. As an alternative to this approach, we developed a physically motivated heat source model, which prescribes the heat input as a body load directly. The new model also relies on prior CFD simulations to identify the melting isotherm. We demonstrate numerical results of the new heat source model on boundary-value problems from the field of laser beam welding and compare it with the prior CFD simulation and the results of the Lamé curve model and experimental data. KW - Welding simulation KW - Heat source models KW - Laser beam welding KW - Thermal analysis PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-600899 DO - https://doi.org/10.3390/met14040430 VL - 14 IS - 4 SP - 1 EP - 26 PB - MDPI CY - Basel AN - OPUS4-60089 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Srinivasan, Krishnanand A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael T1 - Laser Metal Deposition of Rene 80—Microstructure and Solidification Behavior Modelling N2 - New developments in nickel-based superalloys and production methods, such as the use of additive manufacturing (AM), can result in innovative designs for turbines. It is crucial to understand how the material behaves during the AM process to advance the industrial use of these techniques. An analytical model based on reaction–diffusion formalism is developed to better explain the solidification behavior of the material during laser metal deposition (LMD). The well-known Scheil–Gulliver theory has some drawbacks, such as the assumption of equilibrium at the solid–liquid interface, which is addressed by this method. The solidified fractions under the Scheil model and the pure equilibrium model are calculated using CALPHAD simulations. A differential scanning calorimeter is used to measure the heat flow during the solid–liquid phase transformation, the result of which is further converted to solidified fractions. The analytical model is compared with all the other models for validation. KW - Laser metal deposition KW - Solidification behavior KW - Additive manufacturing KW - Analytical model KW - Nickel‐based superalloy PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-612095 DO - https://doi.org/10.3390/mi15101234 SN - 2072-666X VL - 15 IS - 10 SP - 1 EP - 14 PB - MDPI AN - OPUS4-61209 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Huo, W. A1 - Bakir, Nasim A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael A1 - Wolter, K. T1 - Strain Prediction Using Deep Learning during Solidification Crack Initiation and Growth in Laser Beam Welding of Thin Metal Sheets N2 - The strain field can reflect the initiation time of solidification cracks during the welding process. The traditional strain measurement is to first obtain the displacement field through digital image correlation (DIC) or optical flow and then calculate the strain field. The main disadvantage is that the calculation takes a long time, limiting its suitability to real-time applications. Recently, convolutional neural networks (CNNs) have made impressive achievements in computer vision. To build a good prediction model, the network structure and dataset are two key factors. In this paper, we first create the training and test sets containing welding cracks using the controlled tensile weldability (CTW) test and obtain the real strain fields through the Lucas–Kanade algorithm. Then, two new networks using ResNet and DenseNet as encoders are developed for strain prediction, called StrainNetR and StrainNetD. The results show that the average endpoint error (AEE) of the two networks on our test set is about 0.04, close to the real strain value. The computation time could be reduced to the millisecond level, which would greatly improve efficiency. KW - Convolutional neural network KW - Strain fields prediction KW - Laser beam welding KW - Solidification cracking PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-570565 DO - https://doi.org/10.3390/app13052930 VL - 13 IS - 5 SP - 1 EP - 15 PB - MDPI AN - OPUS4-57056 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Huo, Wenjie A1 - Bakir, Nasim A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael A1 - Wolter, Katinka T1 - Detection of solidification crack formation in laser beam welding videos of sheet metal using neural networks N2 - AbstractLaser beam welding has become widely applied in many industrial fields in recent years. Solidification cracks remain one of the most common welding faults that can prevent a safe welded joint. In civil engineering, convolutional neural networks (CNNs) have been successfully used to detect cracks in roads and buildings by analysing images of the constructed objects. These cracks are found in static objects, whereas the generation of a welding crack is a dynamic process. Detecting the formation of cracks as early as possible is greatly important to ensure high welding quality. In this study, two end-to-end models based on long short-term memory and three-dimensional convolutional networks (3D-CNN) are proposed for automatic crack formation detection. To achieve maximum accuracy with minimal computational complexity, we progressively modify the model to find the optimal structure. The controlled tensile weldability test is conducted to generate long videos used for training and testing. The performance of the proposed models is compared with the classical neural network ResNet-18, which has been proven to be a good transfer learning model for crack detection. The results show that our models can detect the start time of crack formation earlier, while ResNet-18 only detects cracks during the propagation stage. KW - Artificial Intelligence KW - Software PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-586116 DO - https://doi.org/10.1007/s00521-023-09004-y SN - 0941-0643 VL - 35 IS - 34 SP - 24315 EP - 24332 PB - Springer Science and Business Media LLC AN - OPUS4-58611 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Straße, Anne A1 - Rethmeier, Michael A1 - Gumenyuk, Andrey T1 - Influence of edge‑deposited layers on mechanical and corrosion properties of laser beam welds of 15 mm thick AISI 2205 duplex stainless steel N2 - AISI 2205 duplex stainless steel is used in a variety of industries, including the chemical and petrochemical industries. This is due to its high tensile strength combined with good ductility and corrosion resistance. However, in laser beam welding, these properties are negatively afected by the high cooling rates typical of the welding process. The resulting higher ferrrite content in the weld metal than in the base material leads to a reduction in the ductility and corrosion resistance of the welded joint. To overcome this problem, in this study, thick plates were coated by direct energy deposition (DED) prior to laser beam welding, whereas a duplex powder mixture containing a higher nickel concentration was used as a coating material. To improve the weld quality for the proposed two-step process, a method of additional material deposition instead of conventional tack weld was investigated. The resulting welded joints showed a well-balanced austenite to ferrite ratio and their properties and microstructure were verifed by metallographic analysis, electron backscatter difraction and Charpy impact testing. Using the standard ASTM G48 test method, it was found that the corrosion resistance of the welds was improved by a factor of four in average compared to the conventionally welded joints. The resulting properties, such as good ductility and corrosion resistance, of the welds with pre-coated edges showed good agreement with those of the base metal and confrmed the proposed two-step process as a promising alternative to the conventional approaches for welding thick duplex stainless steel plates. KW - Laser metal deposition KW - Laser beam welding KW - Duplex steels PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-581731 UR - https://rdcu.be/dlb6E DO - https://doi.org/10.1007/s40194-023-01567-7 SN - 0043-2288 SP - 1 EP - 12 PB - Springer AN - OPUS4-58173 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 - Üstündag, Ömer A1 - Bakir, Nasim A1 - Gook, S. A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael T1 - Hybrid laser‑arc welding of laser‑ and plasma‑cut 20‑mm‑thick structural steels N2 - It is already known that the laser beam welding (LBW) or hybrid laser-arc welding (HLAW) processes are sensitive to manufacturing tolerances such as gaps and misalignment of the edges, especially at welding of thick-walled steels due to its narrow beam diameter. Therefore, the joining parts preferably have to be milled. The study deals with the influence of the edge quality, the gap and the misalignment of edges on the weld seam quality of hybrid laser-arc welded 20-mm-thick structural steel plates which were prepared by laser and plasma cutting. Single-pass welds were conducted in butt joint configuration. An AC magnet was used as a contactless backing. It was positioned under the workpiece during the welding process to prevent sagging. The profile of the edges and the gap between the workpieces were measured before welding by a profile scanner or a digital camera, respectively. With a laser beam power of just 13.7 kW, the single-pass welds could be performed. A gap bridgeability up to 1 mm at laser-cut and 2 mm at plasma-cut samples could be reached respectively. Furthermore, a misalignment of the edges up to 2 mm could be welded in a single pass. The new findings may eliminate the need for cost and time-consuming preparation of the edges. KW - Hybrid laser-arc welding KW - Thick-walled steel KW - Edge quality KW - Gap bridgeability KW - Laser cutting KW - Plasma cutting PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-552013 DO - https://doi.org/10.1007/s40194-022-01255-y SN - 0043-2288 VL - 66 SP - 507 EP - 514 PB - Springer AN - OPUS4-55201 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -