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This study presents mechanisms of the evolution of a narrowed region in the weld pool center during deep penetration laser beam welding. In numerous numerical studies presented in this study, it was also found that the local reduction of the weld pool size can cause detrimental effects on the melt flow behavior and the resulting properties of the welds. A particularly large influence of this effect was identified in three aspects. First, the local variation of the solidification sequence of the weld pool causes an increase in the hot-cracking susceptibility due to a locally delayed solidification. Second, it was proven that a change in the local length and width of the weld pool is associated with an adverse impact on the potential flow routes of the molten material that induces stronger local variations of its solidification. Thus, the element mixing, e.g., during the welding with filler materials, is blocked. This leads to a non-homogeneous chemical composition of the final weld and can cause undesired effects on the final material properties. Finally, another observed effect is related to the reduced ability of process pores to reach the top surface. As this type of porosity is usually produced around the keyhole tip, the change of the fluid flow regime above this area plays a significant role in determining the final path of the pores until the premature solidification in the middle of the weld pool captures them. This study summarizes mainly numerical results that were supported by selected experimental validation results.
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.
This article presents a cost-effective and reliable method for welding 30 mm thick sheets of shipbuilding steel EH36. The method proposes to perform butt welding in a two-run technique using hybrid laser arc welding (HLAW) and submerged arc welding (SAW). The HLAW is performed as a partial penetration weld with a penetration depth of approximately 25 mm. The SAWis carried out as a second run on the opposite side. With a SAWpenetration depth of 8 mm, the weld cross-section is closed with the reliable intersection of both passes. The advantages of the proposed welding method are: no need for forming of the HLAW root; the SAW pass can effectively eliminate pores in the HLAWroot; the high stability of the welding process regarding the preparation quality of the weld edges. Plasma cut edges can be welded without lack of fusion defects. The weld quality achieved is confirmed by destructive tests.
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.
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.
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.
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.
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.
Study on the transition behavior of the bulging effect during deep penetration laser beam welding
(2022)
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.
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.