9 Komponentensicherheit
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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 present work deals with the recently confirmed widening of the weld pool interface, known as a bulging effect, and its relevance in high power laser beam welding. A combined experimental and numerical approach is utilized to study the influence of the bulge on the hot cracking formation and the transport of alloying elements in the molten pool. A technique using a quartz glass, a direct-diode laser illumination, a high-speed camera, and an infrared camera is applied to visualize the weld pool geometry in the longitudinal section. The study examines the relevance of the bulging effect on both, partial and complete penetration, as well as for different sheet thicknesses ranging from 8 mm to 25 mm. The numerical analysis shows that the formation of a bulge region is highly dependent on the penetration depth and occurs more frequently during partial penetration above 6 mm and complete penetration above 8 mm penetration depth, respectively. The location of the bulge correlates strongly with the cracking location. The obtained experimental and numerical results reveal that the bulging effect increases the hot cracking susceptibility and limits the transfer of alloying elements from the top of the weld pool to the weld root.
The present contribution is the first research reporting full penetration HLAW joints in 15 mm thick EH36 steel butt T-welds with square grooves on 2F welding position by single-sided welding. The effects of welding parameters were investigated to increase the quality of the joints.
Conditions leading to defect-free full penetration welds fulfilling naval regulations includes a laser power of 12.5 kW, a welding speed of 1.6 m/min and the vertical laser offset distance from the flange of 1 mm. Advanced characterization of selected welds included a microstructural identification by optical microscopy, SEM, and XRD, revealing the presence of acicular, polygonal and Widmanstätten ferrite, lath martensite, and some retained austenite at FZ. Hardness and microhardness mapping tests showed values of 155 HV at base metal and 200 to 380 HV at the fusion zone connecting the web to the flange.
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.
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 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.
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 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.
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 %.
The application of the electromagnetic stirring from an oscillating magnetic field can improve the metal mixing in wire feed laser beam welding. However, the extra parameters introduced in this technique make the selection of an optimal combination of process parameters more difficult. In the current study, besides the commonly concerned magnetic flux density and frequency, the influence of the magnetic field orientation (magnetic field angle) on the transport of filler metal is studied numerically and experimentally. Ex-situ X-ray fluorescence spectrometer measurements are used to map the metal mixing in the final weld. A three-dimensional transient multi-physical model is developed to reveal the deeper physical essence, considering the coupling between heat transfer, fluid flow, keyhole dynamics, element transport and magnetohydrodynamics. The spatial distribution of the laser energy on the keyhole wall is calculated by a ray tracing algorithm. The results show that the magnetic field with smaller angle with respect to the transverse direction provides better penetration capacity, and its stirring effect can reach the lower part of the molten pool. Therefore, the smaller magnetic field angle produces better metal mixing. A constant downward flow is formed at the lower part of the molten pool when magnetic field of 10° angle is applied, which brings the filler metal to the root region. As the magnetic field angle increases to 40°, the beneficial downward flow changes into a constant upward flow, resulting in a concentration of the filler metal in the upper region. This study provides further insight of the underlying physics in the electromagnetically enhanced laser beam welding, which may guide the optimization of parameters to achieve property homogeneity or to avoid potential defects.