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One of the challenges of the high-power hybrid laser welding of thick steels is the sensitivity of the process of the process to manufacturing tolerances. This usually leads to a time-consuming preparation of the welding edges, such as milling. The study deals with the influence of the edge quality of milled and plasma-cut steel made of S355J2 with a wall thickness of 20 mm on the laser hybrid welded seam quality. Furthermore, the gap bridgeability and the tolerances towards edge misalignment was investigated. An AC magnet was used as backing support to prevent sagging and positioned under the workpiece, to generate an upwards directed electromagnetic pressure. The profiles of the edges and the gap on the top and root side were measured using a digital camera. Single-pass laser hybrid welds of plasma-cut edges could be welded using a laser beam power of just 13.7 kW. A gap bridgeability up to 2 mm and misalignment of edges up to 2 mm could be achieved successful. Additionally, the independence of the cutting side and the welding side was shown, so that samples were welded to the opposite side to their cutting. For evaluation of internal defects or irregularities, X-ray images were carried out. Charpy impact strength tests were performed to determine the toughness of the welds.
Wire arc additive manufacturing enables the production of near-net shape large-volume metallic components leveraging an established industrial base of welding and cladding technology and adapting it for layer-wise material deposition. However, the complex relationship between the process parameters and resulting mechanical properties of the components still remains challenging. In case of high-strength Al-Mg-Si aluminum alloys, no commercial filler wires are yet available due the high susceptibility of solidification cracking as well as the necessary efforts to obtain acceptable mechanical properties. To address this need, we evaluated a novel filler wire based on AlMg0.7Si doped with a Ti5B1 master alloy to foster fine equiaxed grains within the deposited metal. The correlation between the process parameters and component quality was examined by analyzing the size and distribution of pores as well as the grain morphology. Furthermore, we evaluated the influence of different post-weld heat treatment strategies to achieve mechanical properties corresponding to the reference wrought material. We demonstrated that fine equiaxed grains in the weld metal reduced the susceptibility of solidification cracking significantly. The novel AlMg0.7Si-TiB (S Al 6063-TiB) filler wire facilitated wire arc additive manufacturing of high-strength aluminum components with mechanical properties that were almost as superior as the corresponding wrought base material.
The application of magnesium (Mg) inevitably involves dissimilar welding with steel. A novel solid state spot welding method, refill friction stir spot welding (refill FSSW), was utilized to weld AZ31 Mg alloy to galvanized DP600 steel. Although Mg/Fe is an immiscible alloy system, defect-free welds with high strength were successfully obtained in a wide parameter window. The results of microstructure, interfacial reactions, and mechanical properties are reported to reveal the underlying joining mechanism. Due to the melting of Zn coating and subsequent Mg-Zn reactions, Mg-Zn eutectic and intermetallic compounds were detected within welds. Heterogeneous interfacial reactions occur along Mg/steel interface, and the relationship between interfacial structure and fracture behavior was investigated. The joining mechanism is associated with Zn coating and Fe-Al layer: 1) the presence of Zn coating is beneficial for achieving high-quality welding between Mg and steel, it protects the interface from oxidation and contributes to brazing of the weld; 2) the Al present in Mg alloy reacts with Fe, resulting in the growth of Fe-Al layer, which contributes to the diffusion bonding in the interface. The overall results clearly show that Refill FSSW is a competitive welding method for joining Mg and galvanized steel.
Cast magnesium alloys welds produced by refill friction stir spot welding (refill FSSW) show low lap shear strength (LSS) and constantly fail in stirred zone (SZ) shear mode. The cause is most probably related to the heavily textured microstructure. Here, to re-engineer the resulting microstructure, we pro- pose a novel process variant, the differential rotation refill FSSW (DR-refill FSSW). DR-refill FSSW stim- ulates discontinuous dynamic recrystallization and produces a bimodal microstructure with weakened texture. Therefore, the deformation incompatibility between SZ and thermal-mechanically affected zone is avoided. The welds have 50% higher LSS than that of standard refill FSSW welds, and fail in a different failure mode, i.e., SZ pull-out mode. DR-refill FSSW provides a new and effective strategy for improving the performance of spot welds based on microstructural engineering.
Effects on crack formation of additive manufactured Inconel 939 sheets during electron beam welding
(2021)
The potential of additive manufacturing for processing precipitation hardened nickel-base superalloys, such as Inconel 939 is considerable, but in order to fully exploit this potential, fusion welding capabilities for additive parts need to be explored. Currently, it is uncertain how the different properties from the additive manufacturing process will affect the weldability of materials susceptible to hot cracking. Therefore, this work investigates the possibility of joining additively manufactured nickel-based superalloys using electron beam welding. In particular,
the influence of process parameters on crack formation is investigated. In addition, hardness measurements are performed on cross-sections of the welds. It is shown that cracks at the seam head are enhanced by Welding speed and energy per unit length and correlate with the hardness of the weld metal. Cracking parallel to the weld area shows no clear dependence on the process variables that have been investigated, but is related to the hardness of the heat-affected zone.
In this study, steel-glass experiments were conducted to observe the melt pool geometry using a high-speed camera. The high-speed recordings and optical flow analysis show that two main flows take place in form of vortices. The lower vortex drives the melt backwards from the front keyhole wall and thus causes an extension of the melt pool, which is called bulging. This bulging promotes solidification cracking by forming a closed area filled with melt and the accumulation of impurities in the final solidification phase, resulting in low-melting phases which are under tensile stress at the end of solidification.
The increasing adoption of Open Science principles has been a prevalent topic in the welding science community over the last years. Providing access to welding knowledge in the form of complex and complete datasets in addition to peer-reviewed publications can be identified as an important step to promote knowledge exchange and cooperation. There exist previous efforts on building data models specifically for fusion welding applications; however, a common agreed upon implementation that is used by the community is still lacking. One proven approach in other domains has been the use of an openly accessible and agreed upon file and data format used for archiving and sharing domain knowledge in the form of experimental data. Going into a similar direction, the welding community faces particular practical, technical, and also ideological challenges that are discussed in this paper. Collaboratively building upon previous work with modern tools and platforms, the authors motivate, propose, and outline the use of a common file format specifically tailored to the needs of the welding research community as a complement to other already established Open Science practices. Successfully establishing a culture of openly accessible research data has the potential to significantly stimulate progress in welding research.
This article is devoted to the study of the bulging effect in deep penetration laser beam welding. The numerical results of the investigations are based upon experimental results from previous studies to reveal the relationship between the bulging effect and the hot cracking formation, as well as the mixing of alloying elements in the weld pool. The widening of the molten pool in its center area can be observed in full penetration as well as in partial penetration welds on 8 mm and 12 mm thick structural steel plates, respectively. The weld pool shape is extracted from the simulations to evaluate the extent of the necking of the solidification line as well as the bulging phenomena and its influence on the hot cracking phenomena. Relying on an earlier numerical study utilizing a fixed keyhole, simulation models considering a dynamic keyhole are developed thereto. Additionally, the mixing behavior of alloying elements during partial penetration is investigated. The link between the bulge and the studied phenomena is found to be significant.
the joints throughout the structure. The flat units are constituted by butt welded flat plates, on which longitudinal T-welded reinforcements are placed to rigidize the structure. Among the different welding technologies, Hybrid Laser Arc Welding (HLAW) is becoming a mature process, profitable and highly productive. In addition, more innovative welding equipment are being developed nowadays, offering greater work flexibility, and raising expectations of achieving better quality, and economic viability. Another key point of HLAW to keep in mind is that structural distortions are reduced, resulting in decreasing the cost and time of straightening work.
In the present contribution, the influence of HLAW parameters on the quality of fillet joints of naval steel has been analysed. Experimental HLAW tests were performed with a high power disk laser to join EH36 naval steel plates, with a T configuration. The influence of different processing parameters has been analysed, as the laser power, welding speed, wire feed rate and the configuration of the HLAW processes (including head angle and laser/arc leading process). In addition, FEM simulations were carried out in order to estimate residual stresses and distortion of welded part. The distortion values provided by FEM presented excellent agreement with the measured experimental results. To evaluate the welds, non destructive tests including X-ray tests, metallographic analysis of cross sections, and microhardness mapping tests were performed.
Full penetration 8 mm T welds were obtained for the first time at an industrially applicable 2F position with a reasonable HLAW head angle, in one single step without sealing root, and using zero gap square groove Edge preparation. The present contribution presents welding rates up to 2.2 m/min for 2F T-joints of this steel thickness, a much higher processing velocity than previously reported for industrial applications.
Additive manufacturing, and therefore directed energy deposition, is
gaining more and more interest from industrial users. However, quality assurance for the components produced is still a challenge. Machine learning, especially using artificial neuronal networks, is a potential method for ensuring a high-quality standard. Based on process Parameters and monitoring data, part quality can be predicted. A further advantage is the ability to constantly learn and adopt to slight process changes.
First tests using artificial neural networks focus on the prediction of track geometry. The results show that even a small data set is enough to provide high accuracy in the predictions. In this work, an artificial neural network for the predictive analysis of relative density in laser powder cladding has been developed. A central composite experimental design is used to generate 19 data sets. Input variables are laser power, feed rate and powder mass flow. Cubes are built up where density is considered as a target value. Several neural networks are trained and evaluated with these data sets. Different topologies and initial weights are considered. The best network reaches a confidence level of around 90 % for the prediction of relative density based on the process parameters. Finally, the optimization of the generalization performance is investigated. To this purpose, methods of variation in error limit as well as cross-validation are applied. In this way, density is predictable by an artificial neural network with an accuracy of about 95 %.