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The contactless magnetohydrodynamic technology has been considered as a potential and promising method to improve the weld qualities of deep penetration laser beam welding. In this paper, numerical investigations are conducted to study the influence of the auxiliary oscillating magnetic field on the porosity suppression in laser beam welding of 5754 aluminum alloy. To obtain a deeper insight into the suppression mechanism, a three-dimensional transient multi-physical model is developed to calculate the heat transfer, fluid flow, keyhole dynamic, and magnetohydrodynamics. A ray tracing algorithm is employed to calculate the laser energy distribution on the keyhole wall. A time-averaged downward Lorentz force is produced by an oscillating magnetic field. This force acts in the molten pool, leading to a dominant downward flow motion in the longitudinal section, which blocks the bubble migration from the keyhole tip to the rear part of the molten pool. Therefore, the possibility for the bubbles to be captured by the solidification front is reduced. The electromagnetic expulsive force provides an additional upward escaping speed for the bubbles of 1 m/s ~ 5 m/s in the lower and middle region of the molten pool. The simulation results are in a good agreement with experimental measurements. Based on the results obtained in this study, a better understanding of the underlying physics in laser beam welding enhanced by an auxiliary oscillating magnetic field can be provided and thus the welding process can be further optimized reducing the porosity formation.
In this study, we present an enhanced deep learning framework for the prediction of porosity based on thermographic in-situ monitoring data of laser powder bed fusion processes. The manufacturing of two cuboid specimens from Haynes 282 (Ni-based alloy) powder was monitored by a short-wave infrared camera. We use thermogram feature data and x-ray computed tomography data to train a convolutional neural network classifier. The classifier is used to perform a multi-class prediction of the spatially resolved porosity level in small sub-volumes of the specimen bulk.
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. Firstly, 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. Secondly, 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 which were supported by selected experimental validation results.
In additive manufacturing (AM) Laser Metal Deposition (LMD), parts are built by welding layers of powder feedstock onto a substrate. Applications for steel powders include forging tools and structural components for various industries. For large parts, the choice of tool-paths influences the build-rate, the part performance and the distortions in a highly geometry-dependent manner. With weld-path lengths in the range of hundreds of meters, a reliable, automated tool path generation is essential for the usability of LMD processes.
In this contribution, automated tool-path generation approaches are shown and their results are discussed for arbitrary geometries. The investigated path strategies are the classical approaches: “Zig-zag-” and “contour-parallel-strategies”. After generation, the tool-paths are automatically formatted into g-code for experimental build-up and ASCII for a numerical simulation model. Finally, the tool paths are discussed in regards to volume-fill, microstructure and porosity for the experimental samples.
This work presents a part of the IGF project 18737N “Welding distortion simulation” (FOSTA P1140)
The mixture of ammonium nitrate (AN) prills and fuel oil (FO), usually called ANFO, is extensively used in the mining industry as a bulk industrial explosive. One of the major performance predictors of ANFO mixtures is the fuel oil retention, which is itself governed by the porosity of the AN prills. Standardised tests routinely used to assess oil retention face several important limitations; the first being the difficulty to cover the wide range of porosity contents and morphologies from different types of ammonium nitrate prills; the second being the inability to evaluate the closed porosity, which is an important factor regarding the sensitivity of the explosive to detonation. In this study, we present how X-ray computed tomography (XCT), and the associated advanced data processing workflow, can be used to fully characterise the structure and morphology of AN prills. We show that structural parameters such as volume fraction of the different phases and morphological parameters such as specific surface area and shape factor can be reliably extracted from the XCT data, and that there is a good agreement with the measured oil retention values. XCT can therefore be employed to non-destructively and accurately evaluate and characterise porosity in ammonium nitrate prills.