Detection of molten pool state using ResNet50 and control of molten pool penetration in CO2 gas shielded arc welding

Abstract In V groove butt welding with a ceramic backing material, the molten pool should penetrate well. Regardless of the gap fluctuations, the molten pool must maintain a good penetration shape. For this purpose, the molten pool state is detected using ResNet50 as one of the deep learning. The molten pool using a CMOS camera is taken. Fundamental experiments are performed, and images are collected for learning of ResNet50. Good estimation results are obtained for untraining data. The gap and its center are detected processing the molten pool images. The seam tracking is carried out using PI controller, with inputs being the difference between the wire tip and the gap center, and the output is Y axis position. The weaving width is adjusted to fit the gap. The molten pool state is controlled adjusting the travel speed of the welding torch, to keep constant the arc position, because the state of the molten pool depends on the arc position. If there is gap fluctuation as the disturbance and the reference of the arc position is same, it is difficult to get the same penetration of the molten pool. Therefore, the reference according to the output of ResNet50 is adjusted. Molten pool control is based on PI controller with the input is the difference between the arc position and its reference, and the output is the travel speed. The control performance is verified in a case where the gap varies from 7 to 3 mm, and good results are obtained.