In this study, microstructures of weldment produced using carbon steel A516 grade 60 were analysed via a deep learning approach to measure the fraction of acicular ferrite which considerably influences on mechanical properties of carbon steel. The fully convolutional network was used to conduct the image segmentation. Submerged arc welding was used for welding, and the dataset was constructed using optical microscope. The model was compiled with ResNet, which is the state-of-the-art classifier used as an encoder. The model is trained to distinguish acicular ferrite from microstructures of dataset images and then estimate its accuracy. As a result, the mean intersection over union, which is a metric commonly used to evaluate image segmentation, was shown to be higher than 85%.
In this paper, the tandem flux-cored arc welding process was enhanced to improve weld productivity. Additional filler-wire of opposite polarity was used to prevent deterioration of weld quality that occurred due to arc interaction at two electrodes. Droplet transfer is one of the main factors which determine the quality of the weldment. In the tandem process, it is difficult to control droplet transfer due to arc interaction. In the hybrid welding process developed for stabilising the molten pool, the arc interaction and droplet transfers of the two electrodes in the hybrid process were investigated according to the feeding location and applied current of the fillerwire. This presented a method for improving the penetration depth and bead appearance of the weldment.
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