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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 recent years, it was shown that the introduction of additional oscillating and permanent magnetic fields to laser beam and laser-arc hybrid welding can bring several beneficial effects.
Examples are a contactless weld pool support for metals of high thickness suffering from severe drop-out when being welded conventionally or an enhanced stirring to improve the mixing of added filler material in the depth of the weld pool to guarantee homogeneous resulting mechanical properties of the weld. The latest research results show the applicability to various metal types over a wide range of thicknesses and welding conditions. The observations made were demonstrated in numerous experimental studies and a deep understanding of the interaction of the underlying physical mechanisms was extracted from numerical calculations.
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.
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.
The study deals with the determination of the influence of an externally applied oscillating magnetic field on the melt pool dynamics in high power laser beam and hybrid laser arc welding processes. An AC magnet was positioned under the workpiece which is generating an upward directed electromagnetic force to counteract the formation of the droplets. To visualise the melt flow characteristics, several experiments were carried out using a special technique with mild steel from S355J2 with a plate thickness of up to 20 mm and a quartz glass in butt configuration. The profile of the keyhole and the melt flow were recorded with a highspeed camera from the glass side. Additionally, the influence of the magnetic field orientation to the welding direction on the filler material dilution on laser hybrid welding was studied with variating oscillation frequency. The element distribution over the whole seam thickness was measured with X-ray fluorescence (XRF). The oscillation frequency demonstrated a great influence on the melt pool dynamics and the mixing of the elements of the filler wire. The highspeed recordings showed, under the influence of the magnetic field, that the melt is affected under strong vortex at the weld root, which also avoids the formation of droplets.
The stability of the keyhole decreases for deep penetrated high-power laser beam welding. The keyhole tends to collapse with increasing laser power and e.g. keyhole induced porosity can occur. This study deals with the observation of the keyhole during high-power laser beam welding in partial penetration mode by means of a high-speed camera. A butt configuration of 25 mm thick structural steel and transparent quartz glass was used for the experiments. An oscillating magnetic field was applied perpendicular to the welding direction on the root side of the steel plate. The keyhole was highlighted with a coaxial diode laser. It was ascertained that the stability of the keyhole and the weld penetration depth were increased by applying an oscillating magnetic field with an oscillating frequency of 1.2 kHz and a magnetic flux density of 50 mT.
Resistance spot welding is an established joining process for the production of safetyrelevant components in the automotive industry. Therefore, consecutive process monitoring is essential to meet the high quality requirements. Artificial neural networks can be used to evaluate the process parameters and signals, to ensure individual spot weld quality. The predictive accuracy of such algorithms depends on the provided training data set, and the prediction of untrained data is challenging. The aim of this paper was to investigate the extrapolation capability of a multi-layer perceptron model. That means, the predictive performance of the model was tested with data that clearly differed from the training data in terms of material and coating composition. Therefore, three multi-layer perceptron regression models were implemented to predict the nugget diameter from process data. The three models were able to predict the training datasets very well. The models, which were provided with features from the dynamic resistance curve predicted the new dataset better than the model with only process parameters. This study shows the beneficial influence of process signals on the predictive accuracy and robustness of artificial neural network algorithms.
Especially, when predicting a data set from outside of the training space.
The present work is devoted to the numerical analysis of the high-power laser beam welding of thick sheets at different welding speeds. A three-dimensional transient multi-physics numerical model is developed, allowing for the prediction of the keyhole geometry and the final penetration depth. Two ray tracing algorithms are implemented and compared, namely a standard ray tracing approach and an approach using a virtual mesh refinement for a more accurate calculation of the reflection points. Both algorithms are found to provide sufficient accuracy for the prediction of the keyhole depth during laser beam welding with process speeds of up to 1.5 m/min. However, with the standard algorithm, the penetration depth is underestimated by the model for a process speed of 2.5 m/min due to a trapping effect of the laser energy in the top region. In contrast, the virtually refined ray tracing approach results in high accuracy results for process speeds of both 1.5 m/min and 2.5 m/min. A detailed study on the trapping effect is provided, accompanied by a benchmark including a predefined keyhole geometry with typical characteristics for the high-power laser beam welding of thick plates at high process speed, such as deep keyhole, inclined front keyhole wall, and a hump.
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.
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 %.