Abstract In this paper, the microstructure, mechanical properties, and corrosion behavior of friction stir welded (FSW) joints of the two aluminum alloys 6005A‐5083 were investigated and their correlation was discussed. In contrast to FSW joints of the same aluminum material, this results in a “V” shape curve of hardness distributed nonsymmetrically along the weld. The lowest hardness area occurs at the interface between the heat‐affected zone and the thermo‐mechanically affected zone of 6005A (6‐HAZ and 6‐TMAZ) due to the transformation of the β" phase under the influence of heat input during the stir friction process. This also leads to all tensile specimens fracturing in this area. The corrosion behavior of the FSW joint in acidic solution containing Cl − was determined by an exfoliation corrosion (EXCO) test, an intergranular corrosion (IGC) test, and potentiodynamic polarization measurements, based on the potential application in acid rain environment. The results showed that the corrosion resistance order in the acidic solution is: 6‐HAZ > NZ > 6‐BM > 5‐HAZ > 5‐BM. The 5‐BM has the worst corrosion resistance due to the high corrosion sensitivity of Al 3 Mg 2 in acidic solution. However, good corrosion resistances are shown in NZ and 6‐HAZ, which is related to a relatively homogeneous microstructure in NZ and a dissolution or coarsening of β" phases in 6‐HAZ because of frictional heat input.
In this study, the influence of repair welding on microstructure evolution, mechanical properties, and corrosion resistance of SUS304-Q345B dissimilar metal active gas arc (MAG) welding plates was investigated via optical microscopy (OM), scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), hardness, tensile, fatigue, intergranular corrosion, and electrochemical tests, in which the zero (R0), primary (R1), and secondary repair welding (R2) were performed by MAG welding. The results showed that, after repair welding, the δ-ferrite morphology in the weld evolved from a continuous dendritic shape to a dispersed worm-like structure and the bar ferrite in the heat-affected zone of SUS304 (HAZSUS304) evolved into short bar ferrite. The grain size of the weld was reduced due to the remelting caused by repair welding. The hardness of the weld increased first and then decreased which was related to the decrease of δ-ferrite morphology and grain size of weld. In the tensile test, all specimens were fractured at Q345B base material (BMQ345B), which revealed that repair welding had little effect on the tensile properties. The fatigue limit strength of R1 didn't reduce significantly, while the fracture position of R2 transferred from the SUS304 base material (BMSUS304) to the fusion line of SUS304 (FLSUS304). The corrosion resistance of R1 weld possessed the best corrosion resistance owing to the finer grain size and better δ-ferrite morphology. The results indicated that repair welding was feasible for the repair and reuse of welded joints, and it was of great importance in engineering applications of welded plates.
Estimating complex fluid motions from successive images, i.e., Particle Image Velocimetry (PIV), is of great research significance in physics and engineering applications. Although deep learning-based methods have made great progress for fluid motion estimation, it still remains a challenge in terms of robustness and generalization ability. To address this issue, we present a novel fluid flow estimator called RFMFlowNet in this paper, to robustly estimate complex fluid motion using attentional Transformer. Concretely, the feature encoder based on Transformer is customized to enhance features of two frames globally and stably. Here we also enlarge the receptive field of the encoder according to the characteristics of the flow image to extract more effective information. Then, efficient global matching is performed using 4D correlation volume. Furthermore, a refined flow field is iteratively predicted using a GRU-based optimizer. Extensive experiments, including on synthetic and real-world images, are performed to assess the proposed approach. Experimental results demonstrate that the RFMFlowNet achieves new state-of-the-art performance on the public dataset. Meanwhile, our model enjoys great robustness and generalization ability to challenging flow images, reasonably predicting the evolution trend of fluid flows.
Motion fields estimated from image data have been widely used in physics and engineering. Time-resolved particle image velocimetry (TR-PIV) is considered as an advanced flow visualization technique that measures multi-frame velocity fields from successive images. Contrary to conventional PIV, TR-PIV essentially estimates a velocity field video that provides both temporal and spatial information. However, performing TR-PIV with high computational efficiency and high computational accuracy is still a challenge for current algorithms. To solve these problems, we put forward a novel deep learning network named Deep-TRPIV in this study, to effectively estimate fluid motions from multi-frame particle images in an end-to-end manner. First, based on particle image data, we modify the optical flow model known as recurrent all-pairs field transforms that iteratively updates flow fields through a convolutional gated recurrent unit. Second, we specifically design a temporal recurrent network architecture based on this optical flow model by conveying features and flow information from previous frame. When N successive images are fed, the network can efficiently estimate N – 1 motion fields. Moreover, we generate a dataset containing multi-frame particle images and true fluid motions to train the network supervised. Eventually, we conduct extensive experiments on synthetic and experimental data to evaluate the performance of the proposed model. Experimental evaluation results demonstrate that our proposed approach achieves high accuracy and computational efficiency, compared with classical approaches and related deep learning models.
In recent years, deep learning has achieved promising results in optical flow estimation. However, these learning-based methods encounter challenges when dealing with occlusions, especially in the two-view setting. To further improve performance in occluded regions, we introduce an optical flow framework named MMAFlow. Specifically, MMAFlow first estimates an occlusion mask and a coarse flow that provide priors of occlusions and large motions, and then passes motion information from non-occluded pixels to occluded pixels by applying occlusion-aware motion aggregation. Moreover, the aggregated motion information will be utilized by subsequent flow optimizers to obtain the final flow. The experimental results on challenging Sintel and KITTI benchmarks verified the superiority of our proposed MMAFlow, which gains a notable reduction of 10.8% in the average end-point error on Sintel final pass compared to the baseline model, and achieves an F1-all error rate of 4.49% on KITTI 2015.
Abstract The effects of the intergranular corrosion (IGC) defect depth on the high circle fatigue performance of tungsten inert gas (TIG)‐welded joints of 6005A–5083 dissimilar aluminum alloys were investigated. After soaking in different IGC solutions for 72 h, corrosion defects of different depths were generated in various areas of the specimen surfaces. It was found that the 5083 base metal exhibited the poorest corrosion resistance, while the welded metal displayed the best corrosion resistance. Fatigue tests carried out after precorrosion revealed that when the IGC depth was shallow, the weld defects on the weld metal were the main source of cracking for fatigue extension. However, when the IGC depth in the 5083 base metal was >248 μm, the corrosion defect acted as the origin of the fatigue crack, and the fatigue life of the sample was significantly reduced. This was attributed mainly to the local high‐stress field at the tip of the corrosion defect. Finally, a corrosion fatigue crack expansion model was established to analyze the influence of the corrosion defect depth on the fatigue lives of welded joints.
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