Ingenieurwissenschaften und zugeordnete Tätigkeiten
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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.
Magnetohydrodynamic technology is increasingly recognized as a promising approach for enhancing the quality of deep penetration laser beam welding. This study employs experimental and numerical methods to investigate the effects of an auxiliary oscillating magnetic field on reducing porosity in the laser beam welding of 5754 aluminum alloy. The experimental results clearly demonstrate a significant reduction in the porosity ratio, thereby validating the efficacy of applying MHD technology in mitigating porosity during the laser beam welding process. In addition, a transient 3D multi-physical model has been developed, integrating the magnetohydrodynamic and metal vapor plume, to gain a more comprehensive understanding of the porosity suppression mechanism. The introduction of an oscillating magnetic field generates a time-averaged downward Lorentz force. This Lorentz force, in turn, induces an electromagnetic expulsive force, which effectively increases the upward escape velocity of bubbles in the molten pool. Furthermore, the molten pool shape is significantly enlarged, which further facilitate the escape of bubbles. The simulation results agree well with the experimental results.
The global demand for concrete is growing, and with it, its carbon footprint. Current literature proposes biochar, a product of pyrolysis, as a possible car-bon sink to reduce the carbon footprint of concrete. This work investigates the microstructure of Portland cement pastes with 0%, 5%, and 25% of the cement replaced with wood biochar, since this should influence its macro-scopic mechanical properties. MIP, gas sorption, NMR, and µ-CT were used to analyze the pore space of the three materials. The combination of these methods, each with different resolution, enables a multi-scale investigation of biochar impact on the microstructure of cement pastes. NMR confirmed that biochar can absorb moisture and, thus, reduces the effective water-to-cement ratio. MIP and gas sorption results show 0% and 5% volume re-placement have similar gel pore structure. The results from µ-CT investiga-tions suggest that biochar may reduce the formation of larger pores. The in-clusion of non-reactive porous particles such as biochar increase the porosity of the material and should act as a weakness in terms of mechanical proper-ties. Overall, this study highlights the need to carefully tailor replacement rates to control the impact of biochar on the microstructure concrete mixtures and sees a strong need for further studies on mechanical properties.
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.
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.