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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.
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.
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.
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.
Motivated by the loss of tensile strength in 9%Ni steel arc-welded joints performed using commercially available Ni-based austenitic filler metals, the viability of retaining tensile strength using an experimentally produced matching ferritic filler metal was confirmed. Compared to the austenitic Ni-based filler metal (685 MPa), higher tensile strength in gas metal arc (GMA) welded joints was achieved using a ferritic filler metal (749 MPa) due to its microstructure being similar to the base metal (645 MPa). The microstructure of hard martensite resulted in an impact energy of 71 J (-196 °C), which was two times higher than the specified minimum value of _>34 J. The
tensile and impact strength of the welded joint is affected not only by its microstructure, but also by the degree of its mechanical mismatch depending on the type of filler metal. Welds with a harder microstructure and less mechanical mismatch are important for achieving an adequate combination of tensile strength and notched impact strength. This is achievable with the cost-effective ferritic filler metal. A more desirable combination of mechanical properties is guaranteed by applying low preheating temperature (200 °C), which is a more practicable and economical solution compared to the high post-weld heat treatment (PWHT) temperature (580 °C) suggested by other research.
Solidification cracking is still a serious problem in laser beam welding, especially in the welding of thick-walled plates. The influence of weld pool geometry on solidification cracking in partial penetration welding of thick plates is investigated within scope of this study. Therefore, a specific experimental setup of steel and quartz glass in butt configuration and on the side with high-speed camera were used to record the weld pool shape. In addition, the influence of laser inclination angle on the weld pool geometry and on solidification crack formation was investigated. The results show a bulge in the weld pool root, which is separated from an upper region by a necking region. This leads to a case where there are three different longitudinal region lengths with different solidification zones. This temporal sequence of solidification strongly promotes the formation of solidification cracks in the weld root.
Laserstrahlhybridschweissen von Türmen für Windkraftanlagen Ökonomische und ökologische Vorteile
(2022)
Das Laserstrahlhybridschweißen ist beim Schweißen von Türmen für Windkraftanlagen eine Alternative zum Unterpulverschweißen von Dickblechen in Mehrlagentechnik und bietet hier ökonomische und ökologische Vorteile. Der industrielle Einsatz des Verfahrens ist jedoch durch prozessspezifische Herausforderungen eingeschränkt. Die im Beitrag beschriebene kontaktlose elektromagnetische Badstütze dient zur Erweiterung des Verfahrenspotenzials im Dickblechbereich >15 mm.
With additive manufacturing in the powder bed, the component size is limited by the installation space. Joint welding of additively manufactured parts offers a possibility to remove this size limitation. However, due to the specific stress and microstructure state in the additively built material, it is unclear to what extent existing evaluation rules of joint welding are also suitable for welds on additive components. This is investigated using laser beam welding of additively manufactured pipe joints. The welds are evaluated by means of visual inspection, metallographic examinations as well as computed tomography. The types of defects found are comparable to conventional components. This is an indicator that existing evaluation regulations also map the possible defects occurring for weld seams on additive components.
The study deals with the influence of the heat input and the resulting cooling times on the microstructure and Charpy impact toughness of single-pass laser hybrid welded 20-mm thick high-strength steel S690QL. The main focus is on the change of the mechanical properties over the entire seam thickness. The cooling times were measured in-situ using a pyrometer and an optical fibre in three different depths of the seam where Charpy impact test specimens were also later taken. Thereby, three different heat inputs from 1.3 kJ/mm to 2 kJ/mm were investigated. Despite the observed decreased values of both t8/5-cooling time and the Charpy impact toughness in the root part of the seam, the required impact toughness of 38 J/cm2 could be reached in dependance on applied heat input, especially at the heat input of 1.6 kJ/mm.
Laser metal deposition (LMD) as an additive manufacturing technique became increasingly important in recent years and thus the demand for component safety. This is the reason, for the need for reliable in-situ defect detection techniques. For laser beam weld seams an optical measurement technique based on an optical flow algorithm was successfully used to define the critical straining conditions that lead to hot cracking. This algorithm was adapted for bead-on-plate weld seams on LMD deposited layers of IN718 alloy while performing external strain on the specimen in an externally loaded hot cacking test facility. The resulting transversal hot cracks along the weld seam were localized via X-Ray inspection and the type of cracking confirmed by Scanning Electron Microscopy (SEM). The strain distribution was measured in the vicinity of the solidification front and correlated to the detected hot cracks. Based on the results this technique could be adopted for LMD experiments.